examples_error.log

ℹ Updating ClinicoPath documentation
Registered S3 methods overwritten by 'ggpp':
  method                  from   
  heightDetails.titleGrob ggplot2
  widthDetails.titleGrob  ggplot2
Registered S3 method overwritten by 'partitions':
  method            from
  print.equivalence lava
Registered S3 method overwritten by 'DoE.base':
  method           from       
  factorize.factor conf.design
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
Registered S3 methods overwritten by 'forecast':
  method       from  
  autoplot.acf useful
  fortify.ts   useful
Registered S3 method overwritten by 'riskRegression':
  method        from 
  nobs.multinom broom
ℹ Loading ClinicoPath
Registered S3 methods overwritten by 'FSA':
  method       from
  confint.boot car 
  hist.boot    car 
Registered S3 methods overwritten by 'fmsb':
  method    from
  print.roc pROC
  plot.roc  pROC
Registered S3 methods overwritten by 'tangram':
  method               from 
  [.impute             Hmisc
  as.data.frame.impute Hmisc
  print.impute         Hmisc
Registered S3 methods overwritten by 'ggfortify':
  method                 from    
  autoplot.Arima         forecast
  autoplot.acf           forecast
  autoplot.ar            forecast
  autoplot.bats          forecast
  autoplot.decomposed.ts forecast
  autoplot.ets           forecast
  autoplot.forecast      forecast
  autoplot.glmnet        parsnip 
  autoplot.stl           forecast
  autoplot.ts            forecast
  fitted.ar              forecast
  fortify.acf            useful  
  fortify.kmeans         useful  
  fortify.ts             forecast
  residuals.ar           forecast
Warning: 'rgl.init' failed, will use the null device.
See '?rgl.useNULL' for ways to avoid this warning.
Registered S3 method overwritten by 'vcdExtra':
  method      from
  print.Kappa vcd 
Registered S3 method overwritten by 'spatstat.explore':
  method   from
  plot.roc fmsb
Registered S3 methods overwritten by 'logspline':
  method               from     
  plot.logspline       polspline
  plot.oldlogspline    polspline
  print.logspline      polspline
  print.oldlogspline   polspline
  summary.logspline    polspline
  summary.oldlogspline polspline
Registered S3 methods overwritten by 'huge':
  method    from            
  plot.roc  spatstat.explore
  plot.sim  lava            
  print.roc fmsb            
  print.sim lava            
Registered S3 method overwritten by 'fit.models':
  method       from 
  vcov.default Hmisc
Warning: replacing previous import ‘ComplexUpset::upset’ by ‘UpSetR::upset’ when loading ‘ClinicoPath’
Warning: replacing previous import ‘caret::precision’ by ‘cutpointr::precision’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘magrittr::set_names’ by ‘purrr::set_names’ when loading ‘ClinicoPath’
Warning: replacing previous import ‘igraph::simplify’ by ‘purrr::simplify’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘purrr::flatten_lgl’ by ‘rlang::flatten_lgl’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘dplyr::lag’ by ‘stats::lag’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘janitor::chisq.test’ by ‘stats::chisq.test’ when loading ‘ClinicoPath’
Warning: replacing previous import ‘pROC::var’ by ‘stats::var’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘igraph::crossing’ by ‘tidyr::crossing’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘DataExplorer::plot_histogram’ by ‘grafify::plot_histogram’ when loading ‘ClinicoPath’
Warning: replacing previous import ‘reReg::plot’ by ‘graphics::plot’ when loading ‘ClinicoPath’
Warning: replacing previous import ‘dplyr::select’ by ‘jmvcore::select’ when loading ‘ClinicoPath’
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Warning: replacing previous import ‘tibble::view’ by ‘summarytools::view’ when loading ‘ClinicoPath’
Serdar Balci MD Pathologist 
 https://www.serdarbalci.com/ 
 
Warning: ── Conflicts ────────────────────────────────────────── ClinicoPath conflicts
──
✖ `calculateHScore` masks `ClinicoPath::calculateHScore()`.
✖ `calculateIHCDistance` masks `ClinicoPath::calculateIHCDistance()`.
✖ `calculateIHCMarkerSummary` masks `ClinicoPath::calculateIHCMarkerSummary()`.
  … and more.
ℹ Did you accidentally source a file rather than using `load_all()`?
  Run `rm(list = c("calculateHScore", "calculateIHCDistance",
  "calculateIHCMarkerSummary", "calculateIHCSilhouette",
  "convertIHCMarkerToNumeric", "convertIHCToNumeric", "formatIHCPValue",
  "getIHCColorPalette", "getIHCSignificanceStars", "getIHCTheme",
  "showIHCDataRequirements", "validateIHCClustering", "validateIHCData"))` to
  remove the conflicts.
── Running 858 example files ──────────────────────────────────── ClinicoPath ──
ℹ Loading ClinicoPath
Serdar Balci MD Pathologist 
 https://www.serdarbalci.com/ 
 

> data("advancedraincloud_data")

> data("advancedraincloud_baseline")

> data("advancedraincloud_endpoint")

> data("advancedraincloud_change")

> str(advancedraincloud_data)
tibble [900 × 15] (S3: tbl_df/tbl/data.frame)
 $ patient_id             : chr [1:900] "PT001" "PT001" "PT001" "PT002" ...
 $ treatment_arm          : Factor w/ 2 levels "Placebo","Drug A": 1 1 1 1 1 1 1 1 1 1 ...
 $ time_point             : Factor w/ 3 levels "Baseline","Week 4",..: 1 2 3 1 2 3 1 2 3 1 ...
 $ visit_number           : int [1:900] 1 2 3 1 2 3 1 2 3 1 ...
 $ age                    : num [1:900] 71 71 71 48 48 48 59 59 59 63 ...
 $ gender                 : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 1 1 1 2 ...
 $ disease_stage          : Factor w/ 2 levels "Early","Advanced": 1 1 1 1 1 1 1 1 1 1 ...
 $ biomarker_status       : Factor w/ 2 levels "Negative","Positive": 1 1 1 1 1 1 2 2 2 1 ...
 $ tumor_size_change      : num [1:900] 0 NA -21.2 0 NA ...
 $ biomarker_level        : num [1:900] 55.6 42.6 42.6 80.7 NA ...
 $ qol_score              : num [1:900] 69.9 63.6 63.6 41 37.9 ...
 $ pain_score             : Ord.factor w/ 11 levels "0"<"1"<"2"<"3"<..: 5 5 NA 5 5 NA 6 6 NA 9 ...
 $ tumor_responder        : Factor w/ 4 levels "Progressive Disease",..: 2 NA 3 2 NA 1 2 3 NA 2 ...
 $ age_group              : Factor w/ 2 levels "< 65 years","≥ 65 years": 2 2 2 1 1 1 1 1 1 1 ...
 $ baseline_biomarker_high: Factor w/ 2 levels "High","Normal": 2 2 2 2 2 2 2 2 2 1 ...

> summary(advancedraincloud_baseline)
  patient_id        treatment_arm      age           gender     disease_stage
 Length:300         Placebo:150   Min.   :19.00   Female:116   Early   :212  
 Class :character   Drug A :150   1st Qu.:47.00   Male  :184   Advanced: 88  
 Mode  :character                 Median :55.00                              
                                  Mean   :54.73                              
                                  3rd Qu.:63.00                              
                                  Max.   :87.00                              
                                                                             
 biomarker_status tumor_size_change biomarker_level     qol_score    
 Negative:183     Min.   :0         Min.   :  11.12   Min.   :20.00  
 Positive:117     1st Qu.:0         1st Qu.:  53.02   1st Qu.:42.75  
                  Median :0         Median :  92.23   Median :52.28  
                  Mean   :0         Mean   : 141.50   Mean   :50.91  
                  3rd Qu.:0         3rd Qu.: 167.30   3rd Qu.:60.43  
                  Max.   :0         Max.   :1584.03   Max.   :80.00  
                                                                     
   pain_score            tumor_responder      age_group  
 6      :57   Progressive Disease:  0    < 65 years:239  
 7      :52   Stable Disease     :300    ≥ 65 years: 61  
 5      :50   Partial Response   :  0                    
 8      :37   Complete Response  :  0                    
 4      :35                                              
 9      :31                                              
 (Other):38                                              
 baseline_biomarker_high
 High  :136             
 Normal:164             
                        
                        
                        
                        
                        

> table(is.na(advancedraincloud_data$tumor_size_change), 
+     advancedraincloud_data$time_point, advancedraincloud_data$treatment_arm)
, ,  = Placebo

       
        Baseline Week 4 Week 12
  FALSE      150    130      97
  TRUE         0     20      53

, ,  = Drug A

       
        Baseline Week 4 Week 12
  FALSE      150    134     119
  TRUE         0     16      31


> with(advancedraincloud_endpoint, table(tumor_responder, 
+     treatment_arm, useNA = "ifany"))
                     treatment_arm
tumor_responder       Placebo Drug A
  Progressive Disease      42      2
  Stable Disease           44     28
  Partial Response          7     59
  Complete Response         4     30
  <NA>                     53     31

> nrow(advancedraincloud_change)
[1] 130

> data(bujang_table2_validation)

> print(bujang_table2_validation)
  prevalence sensitivity specificity ci_width n_sens_expected n_spec_expected
1       0.05        0.95        0.95      0.1             940             105
2       0.05        0.70        0.70      0.1            3410             379
3       0.10        0.95        0.95      0.1             940             105
4       0.10        0.90        0.90      0.2             440              49
5       0.20        0.90        0.90      0.2             220              49
6       0.50        0.90        0.90      0.2              88              88
7       0.50        0.80        0.80      0.2             140             140
8       0.90        0.95        0.95      0.2              49             941
  n_total_expected
1              940
2             3410
3              940
4              440
5              220
6               88
7              140
8              941

> test1 <- bujang_table2_validation[1, ]

> cat("Prevalence:", test1$prevalence * 100, "%\n")
Prevalence: 5 %

> cat("Targets: Sens =", test1$sensitivity, ", Spec =", 
+     test1$specificity, "\n")
Targets: Sens = 0.95 , Spec = 0.95 

> cat("Expected total N:", test1$n_total_expected, "\n")
Expected total N: 940 

> low_prev <- bujang_table2_validation[bujang_table2_validation$prevalence == 
+     0.05, ]

> high_prev <- bujang_table2_validation[bujang_table2_validation$prevalence == 
+     0.9, ]

> cat("\nLow prevalence (5%) scenarios:\n")

Low prevalence (5%) scenarios:

> print(low_prev[, c("sensitivity", "specificity", "ci_width", 
+     "n_total_expected")])
  sensitivity specificity ci_width n_total_expected
1        0.95        0.95      0.1              940
2        0.70        0.70      0.1             3410

> cat("\nHigh prevalence (90%) scenarios:\n")

High prevalence (90%) scenarios:

> print(high_prev[, c("sensitivity", "specificity", 
+     "ci_width", "n_total_expected")])
  sensitivity specificity ci_width n_total_expected
8        0.95        0.95      0.2              941

> data(cardiac_joint_data)

> library(ggplot2)

> ggplot(cardiac_joint_data, aes(x = visit_time, y = log(nt_probnp), 
+     color = nyha_class)) + geom_smooth(method = "loess") + labs(title = "NT-proBNP Trajectories by NYHA Class", 
+     x = "Time (months)", y = "log(NT-proBNP)")
`geom_smooth()` using formula = 'y ~ x'

> data(cd4_joint_data)

> library(ggplot2)

> ggplot(cd4_joint_data, aes(x = visit_time, y = cd4_count, 
+     color = art_adherence)) + geom_smooth(method = "loess") + 
+     labs(title = "CD4 Trajectories by ART Adherence", x = "Time (months)", 
+         y = "CD4 Count (cells/uL)")
`geom_smooth()` using formula = 'y ~ x'

> data(chisqposttest_test_data)

> str(chisqposttest_test_data)
'data.frame':	300 obs. of  14 variables:
 $ PatientID      : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Treatment      : Factor w/ 2 levels "Standard","Experimental": 1 1 2 1 1 2 1 2 1 1 ...
 $ Response       : Factor w/ 2 levels "No Response",..: 1 1 1 1 1 2 1 2 1 1 ...
 $ Sex            : Factor w/ 2 levels "Male","Female": 2 2 2 2 1 1 2 2 2 2 ...
 $ TumorGrade     : Factor w/ 3 levels "Grade 1","Grade 2",..: 3 2 1 2 3 3 1 2 NA 3 ...
 $ TumorStage     : Factor w/ 3 levels "Stage I","Stage II",..: 3 3 1 2 2 3 2 2 3 3 ...
 $ Institution    : Factor w/ 4 levels "Hospital A","Hospital B",..: 1 1 3 1 3 4 1 1 3 1 ...
 $ QualityScore   : Factor w/ 2 levels "High","Low": 1 2 2 1 1 1 1 1 2 1 ...
 $ RandomVar1     : Factor w/ 3 levels "Group A","Group B",..: 2 2 2 2 1 2 1 1 1 2 ...
 $ RandomVar2     : Factor w/ 2 levels "Type X","Type Y": 1 2 2 1 2 2 1 2 1 1 ...
 $ RareCategory   : Factor w/ 3 levels "Common","Uncommon",..: 2 2 1 1 1 1 2 1 1 1 ...
 $ BinaryOutcome  : Factor w/ 2 levels "Negative","Positive": 1 2 1 1 2 1 2 1 1 1 ...
 $ AgeGroup       : Factor w/ 3 levels "Young","Middle",..: 3 2 1 1 3 2 2 2 1 2 ...
 $ BiomarkerStatus: Factor w/ 2 levels "Negative","Positive": 2 1 2 1 2 2 2 1 1 1 ...
 - attr(*, "description")= chr "Test dataset for chisqposttest function with known associations"
 - attr(*, "associations")=List of 4
  ..$ strong  : chr "Treatment -> Response"
  ..$ moderate: chr [1:2] "TumorGrade -> TumorStage" "AgeGroup -> BiomarkerStatus"
  ..$ weak    : chr [1:2] "Institution -> QualityScore" "RareCategory -> BinaryOutcome"
  ..$ none    : chr "RandomVar1 ⊥ RandomVar2"
 - attr(*, "created")= Date[1:1], format: "2025-07-02"
 - attr(*, "sample_size")= num 300

> chisqposttest(data = chisqposttest_test_data, rows = "Treatment", 
+     cols = "Response", posthoc = "bonferroni")

 CHI-SQUARE POST-HOC TESTS

 <div style='padding: 15px; background-color: #fff3cd; border: 1px
 solid #ffc107; color: #856404;'>Warning:33% of cells have expected
 counts < 5. Chi-square test assumptions violated. Use Fisher's exact
 test for more reliable results.

 Chi-Square Test Results                        
 ────────────────────────────────────────────── 
   Statistic     Value       df    p-value      
 ────────────────────────────────────────────── 
   Chi-Square    66.63312     2    < .0000001   
 ────────────────────────────────────────────── 


 <table style="border-collapse: collapse; width: 100%; margin: 15px 0;
 font-family: &#39;Segoe UI&#39;, system-ui, sans-serif; font-size:
 13px; background-color: white; box-shadow: 0 1px 3px
 rgba(0,0,0,0.1);">

 <tr style="background-color: #e3f2fd;">
 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #f8f9fa;">
 <div style="font-weight: bold; color: #495057;">
 Response →
 <br/>
 Treatment ↓


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Response
 <div style="font-size: 13px; color: #212529;">No Response


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Response
 <div style="font-size: 13px; color: #212529;">Response






 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Treatment
 <div style="font-size: 13px; color: #212529;">Standard


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">88
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">28


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Treatment
 <div style="font-size: 13px; color: #212529;">Experimental


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">49
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">130


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Treatment
 <div style="font-size: 13px; color: #212529;">NA


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">2
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">3



 <div style="padding: 10px; background-color: #e3f2fd; border-left: 4px
 solid #1976d2; margin: 8px 0;">
 Method notice:
 Pairwise comparisons with expected cell counts < 5 are automatically
 analysed with Fisher's exact test; the reported p-values use that
 exact method.

 Pairwise Comparison Results                                                                                                                    
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison                  Test Method       Chi-Square    p-value       Adj. p-value    Effect Size (Phi)    95% CI (Phi)    Significant   
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Standard vs Experimental    Chi-square        66.5303419    < .0000001    < .0000001           0.47500000                    Yes           
   Standard vs NA              Fisher's exact     3.2350414     0.1056590       0.4226362           0.16400000                    No            
   Experimental vs NA          Fisher's exact     0.3870168     0.6181533       1.0000000           0.04600000                    No            
   No Response vs Response     Fisher's exact    66.6331188    < .0000001    < .0000001           0.47100000                    Yes           
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


> chisqposttest(data = chisqposttest_test_data, rows = "TumorGrade", 
+     cols = "TumorStage", posthoc = "fdr")

 CHI-SQUARE POST-HOC TESTS

 <div style='padding: 15px; background-color: #fff3cd; border: 1px
 solid #ffc107; color: #856404;'>Warning:25% of cells have expected
 counts < 5. Chi-square test assumptions violated. Use Fisher's exact
 test for more reliable results.

 Chi-Square Test Results                        
 ────────────────────────────────────────────── 
   Statistic     Value       df    p-value      
 ────────────────────────────────────────────── 
   Chi-Square    109.4691     6    < .0000001   
 ────────────────────────────────────────────── 


 <table style="border-collapse: collapse; width: 100%; margin: 15px 0;
 font-family: &#39;Segoe UI&#39;, system-ui, sans-serif; font-size:
 13px; background-color: white; box-shadow: 0 1px 3px
 rgba(0,0,0,0.1);">

 <tr style="background-color: #e3f2fd;">
 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #f8f9fa;">
 <div style="font-weight: bold; color: #495057;">
 TumorStage →
 <br/>
 TumorGrade ↓


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorStage
 <div style="font-size: 13px; color: #212529;">Stage I


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorStage
 <div style="font-size: 13px; color: #212529;">Stage II


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorStage
 <div style="font-size: 13px; color: #212529;">Stage III






 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorGrade
 <div style="font-size: 13px; color: #212529;">Grade 1


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">56
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">24
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">7


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorGrade
 <div style="font-size: 13px; color: #212529;">Grade 2


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">28
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">71
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">22


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorGrade
 <div style="font-size: 13px; color: #212529;">Grade 3


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">6
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">32
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">49


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">TumorGrade
 <div style="font-size: 13px; color: #212529;">NA


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">0
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">3
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">2



 <div style="padding: 10px; background-color: #e3f2fd; border-left: 4px
 solid #1976d2; margin: 8px 0;">
 Method notice:
 Pairwise comparisons with expected cell counts < 5 are automatically
 analysed with Fisher's exact test; the reported p-values use that
 exact method.

 Pairwise Comparison Results                                                                                                                 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison               Test Method       Chi-Square    p-value       Adj. p-value    Effect Size (Phi)    95% CI (Phi)    Significant   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Grade 1 vs Grade 2       Chi-square         35.741905    < .0000001    < .0000001            0.4150000                    Yes           
   Grade 1 vs Grade 3       Chi-square         72.965438    < .0000001    < .0000001            0.6480000                    Yes           
   Grade 1 vs NA            Fisher's exact      9.846232     0.0045976       0.0059112            0.3270000                    Yes           
   Grade 2 vs Grade 3       Chi-square         34.637706    < .0000001    < .0000001            0.4080000                    Yes           
   Grade 2 vs NA            Fisher's exact      2.358454     0.2905058       0.3268190            0.1370000                    No            
   Grade 3 vs NA            Fisher's exact      1.242070     0.5701787       0.5701787            0.1160000                    No            
   Stage I vs Stage II      Fisher's exact     46.531754    < .0000001    < .0000001            0.4600000                    Yes           
   Stage I vs Stage III     Fisher's exact     74.117520    < .0000001    < .0000001            0.6600000                    Yes           
   Stage II vs Stage III    Fisher's exact     28.625696     0.0000014       0.0000021            0.3690000                    Yes           
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


> chisqposttest(data = chisqposttest_test_data, rows = "RandomVar1", 
+     cols = "RandomVar2", posthoc = "bonferroni")

 CHI-SQUARE POST-HOC TESTS

character(0)

 Chi-Square Test Results                       
 ───────────────────────────────────────────── 
   Statistic     Value       df    p-value     
 ───────────────────────────────────────────── 
   Chi-Square    6.359561     2    0.0415948   
 ───────────────────────────────────────────── 


 <table style="border-collapse: collapse; width: 100%; margin: 15px 0;
 font-family: &#39;Segoe UI&#39;, system-ui, sans-serif; font-size:
 13px; background-color: white; box-shadow: 0 1px 3px
 rgba(0,0,0,0.1);">

 <tr style="background-color: #e3f2fd;">
 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #f8f9fa;">
 <div style="font-weight: bold; color: #495057;">
 RandomVar2 →
 <br/>
 RandomVar1 ↓


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RandomVar2
 <div style="font-size: 13px; color: #212529;">Type X


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RandomVar2
 <div style="font-size: 13px; color: #212529;">Type Y






 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RandomVar1
 <div style="font-size: 13px; color: #212529;">Group A


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">47
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">49


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RandomVar1
 <div style="font-size: 13px; color: #212529;">Group B


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">51
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">57


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RandomVar1
 <div style="font-size: 13px; color: #212529;">Group C


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">61
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">35



character(0)

 Pairwise Comparison Results                                                                                                          
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison            Test Method    Chi-Square    p-value      Adj. p-value    Effect Size (Phi)    95% CI (Phi)    Significant   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Group A vs Group B    Chi-square     0.06136889    0.8043453       1.0000000           0.01700000                    No            
   Group A vs Group C    Chi-square     4.14814815    0.0416801       0.1667203           0.14700000                    No            
   Group B vs Group C    Chi-square     5.46676048    0.0193815       0.0775261           0.16400000                    No            
   Type X vs Type Y      Chi-square     6.35956109    0.0415948       0.1663791           0.14600000                    No            
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


> chisqposttest(data = chisqposttest_test_data, rows = "RareCategory", 
+     cols = "BinaryOutcome", posthoc = "fdr")

 CHI-SQUARE POST-HOC TESTS

character(0)

 Chi-Square Test Results                       
 ───────────────────────────────────────────── 
   Statistic     Value       df    p-value     
 ───────────────────────────────────────────── 
   Chi-Square    24.74713     2    0.0000042   
 ───────────────────────────────────────────── 


 <table style="border-collapse: collapse; width: 100%; margin: 15px 0;
 font-family: &#39;Segoe UI&#39;, system-ui, sans-serif; font-size:
 13px; background-color: white; box-shadow: 0 1px 3px
 rgba(0,0,0,0.1);">

 <tr style="background-color: #e3f2fd;">
 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #f8f9fa;">
 <div style="font-weight: bold; color: #495057;">
 BinaryOutcome →
 <br/>
 RareCategory ↓


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">BinaryOutcome
 <div style="font-size: 13px; color: #212529;">Negative


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">BinaryOutcome
 <div style="font-size: 13px; color: #212529;">Positive






 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RareCategory
 <div style="font-size: 13px; color: #212529;">Common


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">144
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">56


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RareCategory
 <div style="font-size: 13px; color: #212529;">Uncommon


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">53
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">28


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">RareCategory
 <div style="font-size: 13px; color: #212529;">Rare


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">3
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">16



character(0)

 Pairwise Comparison Results                                                                                                            
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison              Test Method    Chi-Square    p-value      Adj. p-value    Effect Size (Phi)    95% CI (Phi)    Significant   
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Common vs Uncommon      Chi-square       1.186668    0.2760028       0.2760028           0.06500000                    No            
   Common vs Rare          Chi-square      24.843523    0.0000006       0.0000025           0.33700000                    Yes           
   Uncommon vs Rare        Chi-square      15.392437    0.0000873       0.0001164           0.39200000                    Yes           
   Negative vs Positive    Chi-square      24.747135    0.0000042       0.0000085           0.28700000                    Yes           
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


> chisqposttest(data = chisqposttest_test_data, rows = "Treatment", 
+     cols = "Sex", excl = TRUE)

 CHI-SQUARE POST-HOC TESTS

character(0)

 Chi-Square Test Results                         
 ─────────────────────────────────────────────── 
   Statistic     Value         df    p-value     
 ─────────────────────────────────────────────── 
   Chi-Square    0.02066086     1    0.8857067   
 ─────────────────────────────────────────────── 


 <table style="border-collapse: collapse; width: 100%; margin: 15px 0;
 font-family: &#39;Segoe UI&#39;, system-ui, sans-serif; font-size:
 13px; background-color: white; box-shadow: 0 1px 3px
 rgba(0,0,0,0.1);">

 <tr style="background-color: #e3f2fd;">
 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #f8f9fa;">
 <div style="font-weight: bold; color: #495057;">
 Sex →
 <br/>
 Treatment ↓


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom: 2px;">Sex
 <div style="font-size: 13px; color: #212529;">Male


 <th style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center; background-color: #e3f2fd;">
 <div style="font-weight: bold;">
 <div style="font-size: 11px; color: #6c757d; margin-bottom: 2px;">Sex
 <div style="font-size: 13px; color: #212529;">Female






 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Treatment
 <div style="font-size: 13px; color: #212529;">Standard


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">50
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">62


 <th style="border: 1px solid #e1e5e9; padding: 8px; background-color:
 #e3f2fd; font-weight: bold;">

 <div style="font-size: 11px; color: #6c757d; margin-bottom:
 2px;">Treatment
 <div style="font-size: 13px; color: #212529;">Experimental


 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">81
 <td style="border: 1px solid #e1e5e9; padding: 8px; text-align:
 center;">97



 <div style='padding: 15px; background-color: #fff3cd; border: 1px
 solid #ffc107;'>Post-hoc Testing Not Performed: Overall chi-square
 test is not significant (p = 0.886 ≥ 0.05). Post-hoc pairwise
 comparisons are only valid when the overall test is significant.
 Running pairwise tests after a non-significant omnibus test increases
 Type I error (false positives) and constitutes data dredging.

> data(decision_test)

> head(decision_test)
# A tibble: 6 × 5
  patient_id GoldStandard NewTest    age sex   
  <chr>      <fct>        <fct>    <dbl> <fct> 
1 PT001      Positive     Negative    31 Male  
2 PT002      Positive     Positive    59 Male  
3 PT003      Negative     Negative    69 Male  
4 PT004      Positive     Negative    80 Female
5 PT005      Negative     Negative    38 Male  
6 PT006      Negative     Negative    41 Male  

> data(decisioncompare_common)

> decisioncompare(data = decisioncompare_common, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive", 
+     test2 = "Test2", test2Positive = "Positive", test3Positive = "", 
+     ci = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         83.00000         9.000000     92.00000   
   Test Negative         21.00000        47.000000     68.00000   
   Total                104.00000        56.000000    160.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 1 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               57.50000     49.44874     65.26819   
   True prevalence                   65.00000     57.07166     72.36120   
   Test sensitivity                  79.80769     70.80542     87.04455   
   Test specificity                  83.92857     71.67203     92.37813   
   Diagnostic accuracy               81.25000     74.32805     86.97742   
   Positive predictive value         90.21739     82.24035     95.42839   
   Negative predictive value         69.11765     56.74392     79.76365   
   Proportion of false positives     16.07143      7.62187     28.32797   
   Proportion of false negative      20.19231     12.95545     29.19458   
   False Discovery Rate               9.78261      4.57161     17.75965   
   False Omission Rate               30.88235     20.23635     43.25608   
 ──────────────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         86.00000         11.00000     97.00000   
   Test Negative         18.00000         45.00000     63.00000   
   Total                104.00000         56.00000    160.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               60.62500     52.60254     68.24621   
   True prevalence                   65.00000     57.07166     72.36120   
   Test sensitivity                  82.69231     74.03265     89.40758   
   Test specificity                  80.35714     67.56670     89.76517   
   Diagnostic accuracy               81.87500     75.01803     87.51260   
   Positive predictive value         88.65979     80.61461     94.20113   
   Negative predictive value         71.42857     58.65390     82.10708   
   Proportion of false positives     19.64286     10.23483     32.43330   
   Proportion of false negative      17.30769     10.59242     25.96735   
   False Discovery Rate              11.34021      5.79887     19.38539   
   False Omission Rate               28.57143     17.89292     41.34610   
 ──────────────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                    Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                      79.80769       83.92857     81.25000                     90.21739                     69.11765                     4.965812                    0.2405892   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                  
   Test2                                                                      82.69231       80.35714     81.87500                     88.65979                     71.42857                     4.209790                    0.2153846   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                  
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 79.8% sensitivity (95% CI: [see confidence interval
 table]), 83.9% specificity (95% CI: [see confidence interval table]),
 90.2% positive predictive value, 69.1% negative predictive value, and
 81.2% overall accuracy. The likelihood ratio for positive results was
 4.97 and for negative results was 0.24.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 160 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with
 79.8% sensitivity (95% CI: [see confidence interval table]), 83.9%
 specificity (95% CI: [see confidence interval table]), 90.2% positive
 predictive value, 69.1% negative predictive value, and 81.2% overall
 accuracy. The likelihood ratio for positive results was 4.97 and for
 negative results was 0.24.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 160
 complete cases. Gold standard identified 104 diseased and 56 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_identical)

> decisioncompare(data = decisioncompare_identical, 
+     gold = "GoldStandard", goldPositive = "Positive", test1 = "Test1", 
+     test1Positive = "Positive", test2 = "Test2", test2Positive = "Positive", 
+     test3Positive = "", statComp = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        22.000000         11.00000     33.00000   
   Test Negative         3.000000         64.00000     67.00000   
   Total                25.000000         75.00000    100.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        22.000000         11.00000     33.00000   
   Test Negative         3.000000         64.00000     67.00000   
   Total                25.000000         75.00000    100.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                 
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                     Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                       88.00000       85.33333     86.00000                     66.66667                     95.52239                     6.000000                    0.1406250   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
   Test2                                                                                       88.00000       85.33333     86.00000                     66.66667                     95.52239                     6.000000                    0.1406250   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Statistical Comparison of Test Accuracy                                                                                 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison        Chi-squared    df    p-value        Clinical Interpretation                                         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1 vs Test2            NaN     1          NaN ᵃ    No significant difference (p≥0.1) (Holm-Bonferroni corrected)   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Note. For 2 tests: McNemar's test compares diagnostic CORRECTNESS (agreement with gold standard) between paired
   tests. For 3+ tests: Cochran's Q test provides an overall test, followed by pairwise McNemar's tests with
   Holm-Bonferroni correction for multiple comparisons. Tests examine discordant pairs (cases where one test is
   correct and the other is wrong relative to the gold standard) to determine if differences in accuracy are
   statistically significant.
   ᵃ Small number of discordant pairs (n=0). Results may be unreliable (recommend n≥10).


 Differences with 95% Confidence Intervals                                  
 ────────────────────────────────────────────────────────────────────────── 
   Comparison        Metric         Difference     Lower        Upper       
 ────────────────────────────────────────────────────────────────────────── 
   Test1 vs Test2    Sensitivity      0.00000 ᵃ      0.00000      0.00000   
   Test1 vs Test2    Specificity      0.00000 ᵇ      0.00000      0.00000   
   Test1 vs Test2    Accuracy         0.00000 ᵈ      0.00000      0.00000   
 ────────────────────────────────────────────────────────────────────────── 
   ᵃ Small paired sample/discordant counts; CI may be unstable (n=25,
   discordant counts: 22, 0, 0, 3).
   ᵇ Small paired sample/discordant counts; CI may be unstable (n=75,
   discordant counts: 64, 0, 0, 11).
   ᵈ Small paired sample/discordant counts; CI may be unstable (n=100,
   discordant counts: 86, 0, 0, 14).


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 88% sensitivity (95% CI: [see confidence interval
 table]), 85.3% specificity (95% CI: [see confidence interval table]),
 66.7% positive predictive value, 95.5% negative predictive value, and
 86% overall accuracy. Statistical comparisons using McNemar's test
 revealed significant differences in test performance (detailed results
 in comparison tables). The likelihood ratio for positive results was
 6.00 and for negative results was 0.14.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 100 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy. Statistical
 comparisons between tests were performed using McNemar's test
 comparing diagnostic correctness (agreement with gold standard).

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with 88%
 sensitivity (95% CI: [see confidence interval table]), 85.3%
 specificity (95% CI: [see confidence interval table]), 66.7% positive
 predictive value, 95.5% negative predictive value, and 86% overall
 accuracy. Statistical comparisons using McNemar's test revealed
 significant differences in test performance (detailed results in
 comparison tables). The likelihood ratio for positive results was 6.00
 and for negative results was 0.14.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 100
 complete cases. Gold standard identified 25 diseased and 75 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_imaging)

> decisioncompare(data = decisioncompare_imaging, gold = "Pathology", 
+     goldPositive = "Malignant", test1 = "CT_Scan", test1Positive = "Abnormal", 
+     test2 = "MRI", test2Positive = "Abnormal", test3 = "Biomarker", 
+     test3Positive = "Elevated", radarplot = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         53.00000         33.00000     86.00000   
   Test Negative         15.00000        119.00000    134.00000   
   Total                 68.00000        152.00000    220.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        63.000000         15.00000     78.00000   
   Test Negative         5.000000        137.00000    142.00000   
   Total                68.000000        152.00000    220.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         49.00000         16.00000     65.00000   
   Test Negative         19.00000        136.00000    155.00000   
   Total                 68.00000        152.00000    220.00000   
 ──────────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                               
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                   Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   CT_Scan                                                                                   77.94118       78.28947     78.18182                     61.62791                     88.80597                     3.590018                   0.28175976   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                                 
   MRI                                                                                       92.64706       90.13158     90.90909                     80.76923                     96.47887                     9.388235                   0.08158008   
     → Good balanced performance; Moderate positive evidence; Strong negative evidence                                                                                                                                                                  
   Biomarker                                                                                 72.05882       89.47368     84.09091                     75.38462                     87.74194                     6.845588                   0.31228374   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                    
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, MRI demonstrated optimal diagnostic
 performance with 92.6% sensitivity (95% CI: [see confidence interval
 table]), 90.1% specificity (95% CI: [see confidence interval table]),
 80.8% positive predictive value, 96.5% negative predictive value, and
 90.9% overall accuracy. The likelihood ratio for positive results was
 9.39 and for negative results was 0.08.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 3 tests (CT_Scan, MRI, Biomarker) against
 the gold standard reference using diagnostic accuracy analysis. The
 study included 220 cases with complete data. Performance metrics
 calculated included sensitivity, specificity, positive and negative
 predictive values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, MRI demonstrated optimal diagnostic performance with 92.6%
 sensitivity (95% CI: [see confidence interval table]), 90.1%
 specificity (95% CI: [see confidence interval table]), 80.8% positive
 predictive value, 96.5% negative predictive value, and 90.9% overall
 accuracy. The likelihood ratio for positive results was 9.39 and for
 negative results was 0.08.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using MRI in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>3 diagnostic tests compared using 220
 complete cases. Gold standard identified 68 diseased and 152 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_indeterminate)

> decisioncompare(data = decisioncompare_indeterminate, 
+     gold = "GoldStandard", goldPositive = "Positive", test1 = "Test1", 
+     test1Positive = "Positive", test2 = "Test2", test2Positive = "Positive", 
+     test3Positive = "", excludeIndeterminate = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        41.000000         14.00000     55.00000   
   Test Negative         5.000000        110.00000    115.00000   
   Total                46.000000        124.00000    170.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        40.000000         13.00000     53.00000   
   Test Negative         6.000000        111.00000    117.00000   
   Total                46.000000        124.00000    170.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                 
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                     Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                       89.13043       88.70968     88.82353                     74.54545                     95.65217                     7.894410                    0.1225296   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
   Test2                                                                                       86.95652       89.51613     88.82353                     75.47170                     94.87179                     8.294314                    0.1457109   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 89.1% sensitivity (95% CI: [see confidence interval
 table]), 88.7% specificity (95% CI: [see confidence interval table]),
 74.5% positive predictive value, 95.7% negative predictive value, and
 88.8% overall accuracy. The likelihood ratio for positive results was
 7.89 and for negative results was 0.12.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 170 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with
 89.1% sensitivity (95% CI: [see confidence interval table]), 88.7%
 specificity (95% CI: [see confidence interval table]), 74.5% positive
 predictive value, 95.7% negative predictive value, and 88.8% overall
 accuracy. The likelihood ratio for positive results was 7.89 and for
 negative results was 0.12.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #fff7ed;
 border-left: 4px solid #fdba74; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #ea580c;'> Multi-Level Test
 Variable
 <span style='color: #374151;'>Test1 has 3 levels: Negative, Positive,
 Indeterminate. Only &quot;Positive&quot; treated as positive; all
 others (Negative, Indeterminate) treated as NEGATIVE. This may inflate
 specificity&#x2F;NPV if equivocal results are present. 115
 non-positive cases detected. Consider enabling &quot;Exclude
 Indeterminate&quot; option or using binary variables.<div
 style='background-color: #eff6ff; border-left: 4px solid #93c5fd;
 padding: 12px; margin: 8px 0; border-radius: 4px;'><strong
 style='color: #2563eb;'> Analysis Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 170
 complete cases. Gold standard identified 46 diseased and 124 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_large)

> decisioncompare(data = decisioncompare_large, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive", 
+     test2 = "Test2", test2Positive = "Positive", test3 = "Test3", 
+     test3Positive = "Positive", ci = TRUE, radarplot = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                           
 ─────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total      
 ─────────────────────────────────────────────────────────────── 
   Test Positive        123.00000         45.00000    168.0000   
   Test Negative         15.00000        317.00000    332.0000   
   Total                138.00000        362.00000    500.0000   
 ─────────────────────────────────────────────────────────────── 


 Test 1 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               33.60000     29.46743     37.92787   
   True prevalence                   27.60000     23.72418     31.74303   
   Test sensitivity                  89.13043     82.70655     93.78736   
   Test specificity                  87.56906     83.72189     90.78645   
   Diagnostic accuracy               88.00000     84.82475     90.71660   
   Positive predictive value         73.21429     65.84713     79.74303   
   Negative predictive value         95.48193     92.65753     97.44954   
   Proportion of false positives     12.43094      9.21355     16.27811   
   Proportion of false negative      10.86957      6.21264     17.29345   
   False Discovery Rate              26.78571     20.25697     34.15287   
   False Omission Rate                4.51807      2.55046      7.34247   
 ──────────────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                           
 ─────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total      
 ─────────────────────────────────────────────────────────────── 
   Test Positive        119.00000         51.00000    170.0000   
   Test Negative         19.00000        311.00000    330.0000   
   Total                138.00000        362.00000    500.0000   
 ─────────────────────────────────────────────────────────────── 


 Test 2 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               34.00000     29.85310     38.33742   
   True prevalence                   27.60000     23.72418     31.74303   
   Test sensitivity                  86.23188     79.33706     91.50265   
   Test specificity                  85.91160     81.89564     89.32707   
   Diagnostic accuracy               86.00000     82.64561     88.92105   
   Positive predictive value         70.00000     62.50987     76.77769   
   Negative predictive value         94.24242     91.15463     96.49834   
   Proportion of false positives     14.08840     10.67293     18.10436   
   Proportion of false negative      13.76812      8.49735     20.66294   
   False Discovery Rate              30.00000     23.22231     37.49013   
   False Omission Rate                5.75758      3.50166      8.84537   
 ──────────────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                           
 ─────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total      
 ─────────────────────────────────────────────────────────────── 
   Test Positive        108.00000         37.00000    145.0000   
   Test Negative         30.00000        325.00000    355.0000   
   Total                138.00000        362.00000    500.0000   
 ─────────────────────────────────────────────────────────────── 


 Test 3 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               29.00000     25.05696     33.19343   
   True prevalence                   27.60000     23.72418     31.74303   
   Test sensitivity                  78.26087     70.44333     84.82633   
   Test specificity                  89.77901     86.18746     92.70086   
   Diagnostic accuracy               86.60000     83.29701     89.46210   
   Positive predictive value         74.48276     66.58386     81.35361   
   Negative predictive value         91.54930     88.15589     94.22578   
   Proportion of false positives     10.22099      7.29914     13.81254   
   Proportion of false negative      21.73913     15.17367     29.55667   
   False Discovery Rate              25.51724     18.64639     33.41614   
   False Omission Rate                8.45070      5.77422     11.84411   
 ──────────────────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                 
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                     Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                       89.13043       87.56906     88.00000                     73.21429                     95.48193                     7.170048                    0.1241256   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
   Test2                                                                                       86.23188       85.91160     86.00000                     70.00000                     94.24242                     6.120773                    0.1602591   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
   Test3                                                                                       78.26087       89.77901     86.60000                     74.48276                     91.54930                     7.656874                    0.2421405   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                      
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 89.1% sensitivity (95% CI: [see confidence interval
 table]), 87.6% specificity (95% CI: [see confidence interval table]),
 73.2% positive predictive value, 95.5% negative predictive value, and
 88% overall accuracy. The likelihood ratio for positive results was
 7.17 and for negative results was 0.12.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 3 tests (Test1, Test2, Test3) against the
 gold standard reference using diagnostic accuracy analysis. The study
 included 500 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with
 89.1% sensitivity (95% CI: [see confidence interval table]), 87.6%
 specificity (95% CI: [see confidence interval table]), 73.2% positive
 predictive value, 95.5% negative predictive value, and 88% overall
 accuracy. The likelihood ratio for positive results was 7.17 and for
 negative results was 0.12.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>3 diagnostic tests compared using 500
 complete cases. Gold standard identified 138 diseased and 362 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_missing)

> decisioncompare(data = decisioncompare_missing, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive", 
+     test2 = "Test2", test2Positive = "Positive", test3Positive = "")

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        32.000000         15.00000     47.00000   
   Test Negative         2.000000         77.00000     79.00000   
   Total                34.000000         92.00000    126.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        27.000000         12.00000     39.00000   
   Test Negative         7.000000         80.00000     87.00000   
   Total                34.000000         92.00000    126.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                    
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                        Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                          94.11765       83.69565     86.50794                     68.08511                     97.46835                     5.772549                   0.07028266   
     → Good sensitivity for screening; Moderate positive evidence; Strong negative evidence                                                                                                                                                                  
   Test2                                                                                          79.41176       86.95652     84.92063                     69.23077                     91.95402                     6.088235                   0.23676471   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 94.1% sensitivity (95% CI: [see confidence interval
 table]), 83.7% specificity (95% CI: [see confidence interval table]),
 68.1% positive predictive value, 97.5% negative predictive value, and
 86.5% overall accuracy. The likelihood ratio for positive results was
 5.77 and for negative results was 0.07.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 126 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with
 94.1% sensitivity (95% CI: [see confidence interval table]), 83.7%
 specificity (95% CI: [see confidence interval table]), 68.1% positive
 predictive value, 97.5% negative predictive value, and 86.5% overall
 accuracy. The likelihood ratio for positive results was 5.77 and for
 negative results was 0.07.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #fefce8;
 border-left: 4px solid #fde047; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #ca8a04;'> Missing Data
 Removed
 <span style='color: #374151;'>Removed 24 rows with missing values in
 selected variables. 84% of original data retained (126&#x2F;150
 cases). This may affect prevalence estimates if data are not missing
 completely at random.<div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 126
 complete cases. Gold standard identified 34 diseased and 92 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_perfect)

> decisioncompare(data = decisioncompare_perfect, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "PerfectTest", test1Positive = "Positive", 
+     test2 = "ImperfectTest", test2Positive = "Positive", test3Positive = "", 
+     statComp = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        40.000000         0.000000     40.00000   
   Test Negative         0.000000        80.000000     80.00000   
   Total                40.000000        80.000000    120.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        32.000000         8.000000     40.00000   
   Test Negative         8.000000        72.000000     80.00000   
   Total                40.000000        80.000000    120.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                                                 
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                                                     Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   PerfectTest                                                                                                                100.00000      100.00000    100.00000                    100.00000                    100.00000                          Inf                   0.01227087   
     → Excellent overall performance; Strong positive evidence; Strong negative evidence (zero cell; LR may be unstable)                                                                                                                                                                  
   ImperfectTest                                                                                                               80.00000       90.00000     86.66667                     80.00000                     90.00000                     8.000000                   0.22222222   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                                                      
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Statistical Comparison of Test Accuracy                                                                                                   
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison                      Chi-squared    df    p-value      Clinical Interpretation                                               
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   PerfectTest vs ImperfectTest       14.06250     1    0.0001768    Highly significant difference (p<0.001) (Holm-Bonferroni corrected)   
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Note. For 2 tests: McNemar's test compares diagnostic CORRECTNESS (agreement with gold standard) between paired tests. For 3+
   tests: Cochran's Q test provides an overall test, followed by pairwise McNemar's tests with Holm-Bonferroni correction for multiple
   comparisons. Tests examine discordant pairs (cases where one test is correct and the other is wrong relative to the gold standard)
   to determine if differences in accuracy are statistically significant.


 Differences with 95% Confidence Intervals                                                
 ──────────────────────────────────────────────────────────────────────────────────────── 
   Comparison                      Metric         Difference     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────────────────────── 
   PerfectTest vs ImperfectTest    Sensitivity     20.00000 ᵃ      7.60410     32.39590   
   PerfectTest vs ImperfectTest    Specificity     10.00000 ᵇ      3.42608     16.57392   
   PerfectTest vs ImperfectTest    Accuracy        13.33333 ᵈ      7.25124     19.41542   
 ──────────────────────────────────────────────────────────────────────────────────────── 
   ᵃ Small paired sample/discordant counts; CI may be unstable (n=40, discordant
   counts: 32, 8, 0, 0).
   ᵇ Small paired sample/discordant counts; CI may be unstable (n=80, discordant
   counts: 72, 8, 0, 0).
   ᵈ Small paired sample/discordant counts; CI may be unstable (n=120, discordant
   counts: 104, 16, 0, 0).


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, PerfectTest demonstrated optimal diagnostic
 performance with 100% sensitivity (95% CI: [see confidence interval
 table]), 100% specificity (95% CI: [see confidence interval table]),
 100% positive predictive value, 100% negative predictive value, and
 100% overall accuracy. Statistical comparisons using McNemar's test
 revealed significant differences in test performance (detailed results
 in comparison tables). The likelihood ratio for positive results was
 Inf and for negative results was 0.01.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (PerfectTest, ImperfectTest) against
 the gold standard reference using diagnostic accuracy analysis. The
 study included 120 cases with complete data. Performance metrics
 calculated included sensitivity, specificity, positive and negative
 predictive values, likelihood ratios, and overall accuracy.
 Statistical comparisons between tests were performed using McNemar's
 test comparing diagnostic correctness (agreement with gold standard).

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, PerfectTest demonstrated optimal diagnostic performance
 with 100% sensitivity (95% CI: [see confidence interval table]), 100%
 specificity (95% CI: [see confidence interval table]), 100% positive
 predictive value, 100% negative predictive value, and 100% overall
 accuracy. Statistical comparisons using McNemar's test revealed
 significant differences in test performance (detailed results in
 comparison tables). The likelihood ratio for positive results was Inf
 and for negative results was 0.01.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Use: PerfectTest shows excellent performance for both
 screening and confirmatory testing.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Zero Cell
 Continuity Correction
 <span style='color: #374151;'>Zero cell detected for PerfectTest.
 LR+&#x2F;LR- computed with a 0.5 continuity correction to avoid
 infinite&#x2F;undefined values; interpret cautiously.<div
 style='background-color: #eff6ff; border-left: 4px solid #93c5fd;
 padding: 12px; margin: 8px 0; border-radius: 4px;'><strong
 style='color: #2563eb;'> Analysis Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 120
 complete cases. Gold standard identified 40 diseased and 80 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_poor)

> decisioncompare(data = decisioncompare_poor, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "PoorTest1", test1Positive = "Positive", 
+     test2 = "PoorTest2", test2Positive = "Positive", test3Positive = "", 
+     ci = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         33.00000         27.00000     60.00000   
   Test Negative         18.00000         62.00000     80.00000   
   Total                 51.00000         89.00000    140.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 1 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               42.85714     34.53395     51.48906   
   True prevalence                   36.42857     28.46665     44.97790   
   Test sensitivity                  64.70588     50.06820     77.56938   
   Test specificity                  69.66292     59.00841     78.96506   
   Diagnostic accuracy               67.85714     59.44545     75.49469   
   Positive predictive value         55.00000     41.61191     67.87785   
   Negative predictive value         77.50000     66.79137     86.08628   
   Proportion of false positives     30.33708     21.03494     40.99159   
   Proportion of false negative      35.29412     22.43062     49.93180   
   False Discovery Rate              45.00000     32.12215     58.38809   
   False Omission Rate               22.50000     13.91372     33.20863   
 ──────────────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         30.00000         32.00000     62.00000   
   Test Negative         21.00000         57.00000     78.00000   
   Total                 51.00000         89.00000    140.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               44.28571     35.90149     52.91687   
   True prevalence                   36.42857     28.46665     44.97790   
   Test sensitivity                  58.82353     44.16928     72.41570   
   Test specificity                  64.04494     53.17822     73.94836   
   Diagnostic accuracy               62.14286     53.56271     70.19764   
   Positive predictive value         48.38710     35.49728     61.43609   
   Negative predictive value         73.07692     61.83560     82.49875   
   Proportion of false positives     35.95506     26.05164     46.82178   
   Proportion of false negative      41.17647     27.58430     55.83072   
   False Discovery Rate              51.61290     38.56391     64.50272   
   False Omission Rate               26.92308     17.50125     38.16440   
 ──────────────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                    Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   PoorTest1                                                                  64.70588       69.66292     67.85714                     55.00000                     77.50000                     2.132898                    0.5066414   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                  
   PoorTest2                                                                  58.82353       64.04494     62.14286                     48.38710                     73.07692                     1.636029                    0.6429309   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                  
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, PoorTest1 demonstrated optimal diagnostic
 performance with 64.7% sensitivity (95% CI: [see confidence interval
 table]), 69.7% specificity (95% CI: [see confidence interval table]),
 55% positive predictive value, 77.5% negative predictive value, and
 67.9% overall accuracy. The likelihood ratio for positive results was
 2.13 and for negative results was 0.51.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (PoorTest1, PoorTest2) against the
 gold standard reference using diagnostic accuracy analysis. The study
 included 140 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, PoorTest1 demonstrated optimal diagnostic performance with
 64.7% sensitivity (95% CI: [see confidence interval table]), 69.7%
 specificity (95% CI: [see confidence interval table]), 55% positive
 predictive value, 77.5% negative predictive value, and 67.9% overall
 accuracy. The likelihood ratio for positive results was 2.13 and for
 negative results was 0.51.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using PoorTest1 in combination with
 other tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 140
 complete cases. Gold standard identified 51 diseased and 89 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_rare)

> decisioncompare(data = decisioncompare_rare, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive", 
+     test2 = "Test2", test2Positive = "Positive", test3Positive = "", 
+     pp = TRUE, pprob = 0.05)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        20.000000         29.00000     49.00000   
   Test Negative         2.000000        249.00000    251.00000   
   Total                22.000000        278.00000    300.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        19.000000         21.00000     40.00000   
   Test Negative         3.000000        257.00000    260.00000   
   Total                22.000000        278.00000    300.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                 
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                     Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                       90.90909       89.56835     89.66667                     31.44441                     99.46864                     8.714734                    0.1014969   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
   Test2                                                                                       86.36364       92.44604     92.00000                     37.56757                     99.22963                    11.432900                    0.1475062   
     → Good balanced performance; Strong positive evidence; Moderate negative evidence                                                                                                                                                                    
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test2 demonstrated optimal diagnostic
 performance with 86.4% sensitivity (95% CI: [see confidence interval
 table]), 92.4% specificity (95% CI: [see confidence interval table]),
 37.6% positive predictive value, 99.2% negative predictive value, and
 92% overall accuracy. The likelihood ratio for positive results was
 11.43 and for negative results was 0.15.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 300 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test2 demonstrated optimal diagnostic performance with
 86.4% sensitivity (95% CI: [see confidence interval table]), 92.4%
 specificity (95% CI: [see confidence interval table]), 37.6% positive
 predictive value, 99.2% negative predictive value, and 92% overall
 accuracy. The likelihood ratio for positive results was 11.43 and for
 negative results was 0.15.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test2 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Prevalence
 Source
 <span style='color: #374151;'>PPV&#x2F;NPV will be calculated using
 the supplied population prevalence (pp=TRUE). Confidence intervals are
 unavailable in this mode.<div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 300
 complete cases. Gold standard identified 22 diseased and 278 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_raters)

> decisioncompare(data = decisioncompare_raters, gold = "ConsensusPanel", 
+     goldPositive = "Disease", test1 = "Rater1", test1Positive = "Disease", 
+     test2 = "Rater2", test2Positive = "Disease", test3 = "Rater3", 
+     test3Positive = "Disease", statComp = TRUE)
Performing 3 pairwise comparisons with Holm-Bonferroni correction...
Statistical comparisons completed (3 comparisons, Holm-Bonferroni corrected).

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        35.000000         9.000000     44.00000   
   Test Negative         4.000000       102.000000    106.00000   
   Total                39.000000       111.000000    150.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         29.00000         11.00000     40.00000   
   Test Negative         10.00000        100.00000    110.00000   
   Total                 39.00000        111.00000    150.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         29.00000         12.00000     41.00000   
   Test Negative         10.00000         99.00000    109.00000   
   Total                 39.00000        111.00000    150.00000   
 ──────────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                               
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                   Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Rater1                                                                                    89.74359       91.89189     91.33333                     79.54545                     96.22642                    11.068376                    0.1116139   
     → Good balanced performance; Strong positive evidence; Moderate negative evidence                                                                                                                                                                  
   Rater2                                                                                    74.35897       90.09009     86.00000                     72.50000                     90.90909                     7.503497                    0.2846154   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                    
   Rater3                                                                                    74.35897       89.18919     85.33333                     70.73171                     90.82569                     6.878205                    0.2874903   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                    
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Statistical Comparison of Test Accuracy                                                                                    
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison           Chi-squared    df    p-value      Clinical Interpretation                                           
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Overall (3 tests)       2.979592     2    0.2254187    No significant overall difference among tests (p≥0.05)        ᵃ   
   Rater1 vs Rater2        1.531250     1    0.4318499    No significant difference (p≥0.1) (Holm-Bonferroni corrected)     
   Rater1 vs Rater3        2.206897     1    0.4121845    No significant difference (p≥0.1) (Holm-Bonferroni corrected)     
   Rater2 vs Rater3        0.000000     1    1.0000000    No significant difference (p≥0.1) (Holm-Bonferroni corrected)     
 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Note. For 2 tests: McNemar's test compares diagnostic CORRECTNESS (agreement with gold standard) between paired
   tests. For 3+ tests: Cochran's Q test provides an overall test, followed by pairwise McNemar's tests with
   Holm-Bonferroni correction for multiple comparisons. Tests examine discordant pairs (cases where one test is correct
   and the other is wrong relative to the gold standard) to determine if differences in accuracy are statistically
   significant.
   ᵃ Cochran's Q test shows no significant difference. Pairwise comparisons below may not be meaningful.


 Differences with 95% Confidence Intervals                                    
 ──────────────────────────────────────────────────────────────────────────── 
   Comparison          Metric         Difference     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────────── 
   Rater1 vs Rater2    Sensitivity     15.38462 ᵃ     -1.34142     32.11065   
   Rater1 vs Rater2    Specificity      1.80180 ᵇ     -6.08768      9.69128   
   Rater1 vs Rater2    Accuracy         5.33333 ᵈ     -2.00871     12.67538   
   Rater1 vs Rater3    Sensitivity     15.38462 ᵉ      0.24368     30.52555   
   Rater1 vs Rater3    Specificity      2.70270 ᶠ     -4.97751     10.38292   
   Rater1 vs Rater3    Accuracy         6.00000 ᵍ     -0.97067     12.97067   
   Rater2 vs Rater3    Sensitivity      0.00000 ʰ    -20.10219     20.10219   
   Rater2 vs Rater3    Specificity      0.90090 ⁱ     -7.18897      8.99077   
   Rater2 vs Rater3    Accuracy         0.66667 ʲ     -7.28061      8.61395   
 ──────────────────────────────────────────────────────────────────────────── 
   ᵃ Small paired sample/discordant counts; CI may be unstable (n=39,
   discordant counts: 26, 9, 3, 1).
   ᵇ Small paired sample/discordant counts; CI may be unstable (n=111,
   discordant counts: 91, 11, 9, 0).
   ᵈ Small paired sample/discordant counts; CI may be unstable (n=150,
   discordant counts: 117, 20, 12, 1).
   ᵉ Small paired sample/discordant counts; CI may be unstable (n=39,
   discordant counts: 27, 8, 2, 2).
   ᶠ Small paired sample/discordant counts; CI may be unstable (n=111,
   discordant counts: 91, 11, 8, 1).
   ᵍ Small paired sample/discordant counts; CI may be unstable (n=150,
   discordant counts: 118, 19, 10, 3).
   ʰ Small paired sample/discordant counts; CI may be unstable (n=39,
   discordant counts: 21, 8, 8, 2).
   ⁱ Small paired sample/discordant counts; CI may be unstable (n=111,
   discordant counts: 89, 11, 10, 1).
   ʲ Small paired sample/discordant counts; CI may be unstable (n=150,
   discordant counts: 110, 19, 18, 3).


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Rater1 demonstrated optimal diagnostic
 performance with 89.7% sensitivity (95% CI: [see confidence interval
 table]), 91.9% specificity (95% CI: [see confidence interval table]),
 79.5% positive predictive value, 96.2% negative predictive value, and
 91.3% overall accuracy. Statistical comparisons using McNemar's test
 revealed significant differences in test performance (detailed results
 in comparison tables). The likelihood ratio for positive results was
 11.07 and for negative results was 0.11.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 3 tests (Rater1, Rater2, Rater3) against the
 gold standard reference using diagnostic accuracy analysis. The study
 included 150 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy. Statistical
 comparisons between tests were performed using McNemar's test
 comparing diagnostic correctness (agreement with gold standard).

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Rater1 demonstrated optimal diagnostic performance with
 89.7% sensitivity (95% CI: [see confidence interval table]), 91.9%
 specificity (95% CI: [see confidence interval table]), 79.5% positive
 predictive value, 96.2% negative predictive value, and 91.3% overall
 accuracy. Statistical comparisons using McNemar's test revealed
 significant differences in test performance (detailed results in
 comparison tables). The likelihood ratio for positive results was
 11.07 and for negative results was 0.11.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Rater1 in combination with
 other tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>3 diagnostic tests compared using 150
 complete cases. Gold standard identified 39 diseased and 111 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_screening)

> decisioncompare(data = decisioncompare_screening, 
+     gold = "Biopsy", goldPositive = "Positive", test1 = "ScreeningTest", 
+     test1Positive = "Positive", test2 = "DiagnosticTest", test2Positive = "Positive", 
+     test3Positive = "", pp = TRUE, pprob = 0.15)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        41.000000         35.00000     76.00000   
   Test Negative         1.000000        173.00000    174.00000   
   Total                42.000000        208.00000    250.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        34.000000         20.00000     54.00000   
   Test Negative         8.000000        188.00000    196.00000   
   Total                42.000000        208.00000    250.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                        
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                            Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   ScreeningTest                                                                                      97.61905       83.17308     85.60000                     50.58726                     99.49737                     5.801361                   0.02862648   
     → Excellent for screening (rule-out); Moderate positive evidence; Strong negative evidence                                                                                                                                                                  
   DiagnosticTest                                                                                     80.95238       90.38462     88.80000                     59.77011                     96.41441                     8.419048                   0.21073961   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                             
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, ScreeningTest demonstrated optimal
 diagnostic performance with 97.6% sensitivity (95% CI: [see confidence
 interval table]), 83.2% specificity (95% CI: [see confidence interval
 table]), 50.6% positive predictive value, 99.5% negative predictive
 value, and 85.6% overall accuracy. The likelihood ratio for positive
 results was 5.80 and for negative results was 0.03.<h3 style="color:
 #27ae60; margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (ScreeningTest, DiagnosticTest)
 against the gold standard reference using diagnostic accuracy
 analysis. The study included 250 cases with complete data. Performance
 metrics calculated included sensitivity, specificity, positive and
 negative predictive values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, ScreeningTest demonstrated optimal diagnostic performance
 with 97.6% sensitivity (95% CI: [see confidence interval table]),
 83.2% specificity (95% CI: [see confidence interval table]), 50.6%
 positive predictive value, 99.5% negative predictive value, and 85.6%
 overall accuracy. The likelihood ratio for positive results was 5.80
 and for negative results was 0.03.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Screening Application: ScreeningTest is excellent for initial
 screening due to high sensitivity (low false negative rate).

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Prevalence
 Source
 <span style='color: #374151;'>PPV&#x2F;NPV will be calculated using
 the supplied population prevalence (pp=TRUE). Confidence intervals are
 unavailable in this mode.<div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 250
 complete cases. Gold standard identified 42 diseased and 208 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_small)

> decisioncompare(data = decisioncompare_small, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive", 
+     test2 = "Test2", test2Positive = "Positive", test3Positive = "", 
+     ci = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                           
 ─────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total      
 ─────────────────────────────────────────────────────────────── 
   Test Positive         9.000000         3.000000    12.00000   
   Test Negative         3.000000        15.000000    18.00000   
   Total                12.000000        18.000000    30.00000   
 ─────────────────────────────────────────────────────────────── 


 Test 1 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               40.00000     22.65576     59.39651   
   True prevalence                   40.00000     22.65576     59.39651   
   Test sensitivity                  75.00000     42.81415     94.51394   
   Test specificity                  83.33333     58.58225     96.42149   
   Diagnostic accuracy               80.00000     61.43335     92.28645   
   Positive predictive value         75.00000     42.81415     94.51394   
   Negative predictive value         83.33333     58.58225     96.42149   
   Proportion of false positives     16.66667      3.57851     41.41775   
   Proportion of false negative      25.00000      5.48606     57.18585   
   False Discovery Rate              25.00000      5.48606     57.18585   
   False Omission Rate               16.66667      3.57851     41.41775   
 ──────────────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                           
 ─────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total      
 ─────────────────────────────────────────────────────────────── 
   Test Positive         7.000000         4.000000    11.00000   
   Test Negative         5.000000        14.000000    19.00000   
   Total                12.000000        18.000000    30.00000   
 ─────────────────────────────────────────────────────────────── 


 Test 2 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               36.66667     19.92986     56.14402   
   True prevalence                   40.00000     22.65576     59.39651   
   Test sensitivity                  58.33333     27.66697     84.83478   
   Test specificity                  77.77778     52.36272     93.59080   
   Diagnostic accuracy               70.00000     50.60410     85.26548   
   Positive predictive value         63.63636     30.79047     89.07366   
   Negative predictive value         73.68421     48.79707     90.85342   
   Proportion of false positives     22.22222      6.40920     47.63728   
   Proportion of false negative      41.66667     15.16522     72.33303   
   False Discovery Rate              36.36364     10.92634     69.20953   
   False Omission Rate               26.31579      9.14658     51.20293   
 ──────────────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                    Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                      75.00000       83.33333     80.00000                     75.00000                     83.33333                     4.500000                    0.3000000   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                  
   Test2                                                                      58.33333       77.77778     70.00000                     63.63636                     73.68421                     2.625000                    0.5357143   
     → Limited diagnostic utility - consider combining with other tests                                                                                                                                                                  
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 75% sensitivity (95% CI: [see confidence interval
 table]), 83.3% specificity (95% CI: [see confidence interval table]),
 75% positive predictive value, 83.3% negative predictive value, and
 80% overall accuracy. The likelihood ratio for positive results was
 4.50 and for negative results was 0.30.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 30 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with 75%
 sensitivity (95% CI: [see confidence interval table]), 83.3%
 specificity (95% CI: [see confidence interval table]), 75% positive
 predictive value, 83.3% negative predictive value, and 80% overall
 accuracy. The likelihood ratio for positive results was 4.50 and for
 negative results was 0.30.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #fefce8;
 border-left: 4px solid #fde047; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #ca8a04;'> Small Sample
 Size
 <span style='color: #374151;'>Small sample size (n=30). Confidence
 intervals may be wide and estimates unstable. Minimum recommended:
 n=50-100 for adequate precision in diagnostic accuracy studies.
 Consider collecting additional data for reliable assessment.<div
 style='background-color: #eff6ff; border-left: 4px solid #93c5fd;
 padding: 12px; margin: 8px 0; border-radius: 4px;'><strong
 style='color: #2563eb;'> Analysis Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 30
 complete cases. Gold standard identified 12 diseased and 18 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_test)

> decisioncompare(data = decisioncompare_test, gold = "GoldStandard", 
+     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive", 
+     test2 = "Test2", test2Positive = "Positive", test3Positive = "", 
+     ci = TRUE, statComp = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        62.000000         12.00000     74.00000   
   Test Negative         9.000000        117.00000    126.00000   
   Total                71.000000        129.00000    200.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 1 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               37.00000     30.29698     44.09465   
   True prevalence                   35.50000     28.87838     42.55862   
   Test sensitivity                  87.32394     77.29595     94.03642   
   Test specificity                  90.69767     84.31447     95.10049   
   Diagnostic accuracy               89.50000     84.39813     93.38188   
   Positive predictive value         83.78378     73.38642     91.33010   
   Negative predictive value         92.85714     86.87359     96.68197   
   Proportion of false positives      9.30233      4.89951     15.68553   
   Proportion of false negative      12.67606      5.96358     22.70405   
   False Discovery Rate              16.21622      8.66990     26.61358   
   False Omission Rate                7.14286      3.31803     13.12641   
 ──────────────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         56.00000         18.00000     74.00000   
   Test Negative         15.00000        111.00000    126.00000   
   Total                 71.00000        129.00000    200.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Confidence Intervals                                            
 ──────────────────────────────────────────────────────────────────────── 
   Decision Statistics              Estimate     Lower        Upper       
 ──────────────────────────────────────────────────────────────────────── 
   Apparent prevalence               37.00000     30.29698     44.09465   
   True prevalence                   35.50000     28.87838     42.55862   
   Test sensitivity                  78.87324     67.55973     87.66594   
   Test specificity                  86.04651     78.84665     91.51575   
   Diagnostic accuracy               83.50000     77.61565     88.36169   
   Positive predictive value         75.67568     64.30857     84.90182   
   Negative predictive value         88.09524     81.12595     93.18155   
   Proportion of false positives     13.95349      8.48425     21.15335   
   Proportion of false negative      21.12676     12.33406     32.44027   
   False Discovery Rate              24.32432     15.09818     35.69143   
   False Omission Rate               11.90476      6.81845     18.87405   
 ──────────────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                        
 ──────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total   
 ──────────────────────────────────────────────────────────── 
   Test Positive    .                .                .       
   Test Negative    .                .                .       
   Total            .                .                .       
 ──────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                 
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                     Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                       87.32394       90.69767     89.50000                     83.78378                     92.85714                     9.387324                    0.1397616   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                  
   Test2                                                                                       78.87324       86.04651     83.50000                     75.67568                     88.09524                     5.652582                    0.2455272   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                      
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Statistical Comparison of Test Accuracy                                                                               
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Comparison        Chi-squared    df    p-value      Clinical Interpretation                                         
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1 vs Test2       2.520833     1    0.1123512    No significant difference (p≥0.1) (Holm-Bonferroni corrected)   
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Note. For 2 tests: McNemar's test compares diagnostic CORRECTNESS (agreement with gold standard) between paired
   tests. For 3+ tests: Cochran's Q test provides an overall test, followed by pairwise McNemar's tests with
   Holm-Bonferroni correction for multiple comparisons. Tests examine discordant pairs (cases where one test is
   correct and the other is wrong relative to the gold standard) to determine if differences in accuracy are
   statistically significant.


 Differences with 95% Confidence Intervals                                  
 ────────────────────────────────────────────────────────────────────────── 
   Comparison        Metric         Difference     Lower        Upper       
 ────────────────────────────────────────────────────────────────────────── 
   Test1 vs Test2    Sensitivity      8.45070 ᵃ     -4.34717     21.24858   
   Test1 vs Test2    Specificity      4.65116 ᵇ     -3.05435     12.35668   
   Test1 vs Test2    Accuracy         6.00000 ᵈ     -0.73840     12.73840   
 ────────────────────────────────────────────────────────────────────────── 
   ᵃ Small paired sample/discordant counts; CI may be unstable (n=71,
   discordant counts: 48, 14, 8, 1).
   ᵇ Small paired sample/discordant counts; CI may be unstable (n=129,
   discordant counts: 101, 16, 10, 2).
   ᵈ Small paired sample/discordant counts; CI may be unstable (n=200,
   discordant counts: 149, 30, 18, 3).


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test1 demonstrated optimal diagnostic
 performance with 87.3% sensitivity (95% CI: [see confidence interval
 table]), 90.7% specificity (95% CI: [see confidence interval table]),
 83.8% positive predictive value, 92.9% negative predictive value, and
 89.5% overall accuracy. Statistical comparisons using McNemar's test
 revealed significant differences in test performance (detailed results
 in comparison tables). The likelihood ratio for positive results was
 9.39 and for negative results was 0.14.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 2 tests (Test1, Test2) against the gold
 standard reference using diagnostic accuracy analysis. The study
 included 200 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy. Statistical
 comparisons between tests were performed using McNemar's test
 comparing diagnostic correctness (agreement with gold standard).

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test1 demonstrated optimal diagnostic performance with
 87.3% sensitivity (95% CI: [see confidence interval table]), 90.7%
 specificity (95% CI: [see confidence interval table]), 83.8% positive
 predictive value, 92.9% negative predictive value, and 89.5% overall
 accuracy. Statistical comparisons using McNemar's test revealed
 significant differences in test performance (detailed results in
 comparison tables). The likelihood ratio for positive results was 9.39
 and for negative results was 0.14.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Clinical Consideration: Consider using Test1 in combination with other
 tests for optimal diagnostic accuracy.

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>2 diagnostic tests compared using 200
 complete cases. Gold standard identified 71 diseased and 129 healthy
 cases. Review comparison tables and statistical tests below.

> data(decisioncompare_threetest)

> decisioncompare(data = decisioncompare_threetest, 
+     gold = "GoldStandard", goldPositive = "Positive", test1 = "Test1", 
+     test1Positive = "Positive", test2 = "Test2", test2Positive = "Positive", 
+     test3 = "Test3", test3Positive = "Positive", radarplot = TRUE)

 COMPARE MEDICAL DECISION TESTS

character(0)

 Test 1 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive        56.000000         13.00000     69.00000   
   Test Negative         7.000000        104.00000    111.00000   
   Total                63.000000        117.00000    180.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 2 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         48.00000         13.00000     61.00000   
   Test Negative         15.00000        104.00000    119.00000   
   Total                 63.00000        117.00000    180.00000   
 ──────────────────────────────────────────────────────────────── 


 Test 3 - Recoded Data                                            
 ──────────────────────────────────────────────────────────────── 
                    Gold Positive    Gold Negative    Total       
 ──────────────────────────────────────────────────────────────── 
   Test Positive         51.00000         5.000000     56.00000   
   Test Negative         12.00000       112.000000    124.00000   
   Total                 63.00000       117.000000    180.00000   
 ──────────────────────────────────────────────────────────────── 


 Decision Test Comparison                                                                                                                                                                                                                                          
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test                                                                                              Sensitivity    Specificity    Accuracy     Positive Predictive Value    Negative Predictive Value    Positive Likelihood Ratio    Negative Likelihood Ratio   
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Test1                                                                                                88.88889       88.88889     88.88889                     81.15942                     93.69369                     8.000000                    0.1250000   
     → Good balanced performance; Moderate positive evidence; Moderate negative evidence                                                                                                                                                                           
   Test2                                                                                                76.19048       88.88889     84.44444                     78.68852                     87.39496                     6.857143                    0.2678571   
     → Good specificity for confirmation; Moderate positive evidence                                                                                                                                                                                               
   Test3                                                                                                80.95238       95.72650     90.55556                     91.07143                     90.32258                    18.942857                    0.1989796   
     → Excellent for confirmation (rule-in); Strong positive evidence; Moderate negative evidence                                                                                                                                                                  
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 Stratified Diagnostic Accuracy                                                                              
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
   Subgroup    N    Test    Sensitivity    Specificity    Accuracy     PPV          NPV          OPA         
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────── 


 <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
 solid #3498db;"> Clinical Summary

 Among the tests evaluated, Test3 demonstrated optimal diagnostic
 performance with 81% sensitivity (95% CI: [see confidence interval
 table]), 95.7% specificity (95% CI: [see confidence interval table]),
 91.1% positive predictive value, 90.3% negative predictive value, and
 90.6% overall accuracy. The likelihood ratio for positive results was
 18.94 and for negative results was 0.20.<h3 style="color: #27ae60;
 margin-top: 30px;"> Report Sentences

 <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
 solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
 Section:

 <p style="font-style: italic; line-height: 1.6;">We compared the
 diagnostic performance of 3 tests (Test1, Test2, Test3) against the
 gold standard reference using diagnostic accuracy analysis. The study
 included 180 cases with complete data. Performance metrics calculated
 included sensitivity, specificity, positive and negative predictive
 values, likelihood ratios, and overall accuracy.

 <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
 solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
 Section:

 <p style="font-style: italic; line-height: 1.6;">Among the tests
 evaluated, Test3 demonstrated optimal diagnostic performance with 81%
 sensitivity (95% CI: [see confidence interval table]), 95.7%
 specificity (95% CI: [see confidence interval table]), 91.1% positive
 predictive value, 90.3% negative predictive value, and 90.6% overall
 accuracy. The likelihood ratio for positive results was 18.94 and for
 negative results was 0.20.

 <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
 Recommendations

 <div style="background-color: #fff3cd; padding: 15px; border-radius:
 8px;">

 Confirmatory Application: Test3 is excellent for confirming diagnosis
 due to high specificity (low false positive rate).

 Implementation Note: Results should be interpreted in the context of
 disease prevalence in your clinical population. Consider local
 validation studies before implementation.

 <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
 border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
 Medical Decision Test Comparison

 <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
 style="color: #1565c0; margin-top: 0;"> What This Analysis Does

 <p style="line-height: 1.6; color: #333;">This tool compares the
 diagnostic performance of multiple medical tests against a gold
 standard reference. It systematically evaluates sensitivity,
 specificity, predictive values, likelihood ratios, and overall
 accuracy to help you determine which test performs best for your
 clinical scenario.

 <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #4a7c59; margin-top: 0;"> When to Use This Analysis

 <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
 Comparing new diagnostic methods against established standardsMethod
 Comparison: Evaluating which of several tests performs betterClinical
 Research: Validating biomarkers, imaging techniques, or clinical
 assessmentsQuality Assessment: Measuring agreement between different
 raters or methodsProtocol Development: Optimizing diagnostic
 workflows<div style="background-color: #fff3e0; border: 1px solid
 #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
 style="color: #e65100; margin-top: 0;"> How to Use This Analysis

 <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
 Choose your most reliable reference test (e.g., biopsy, expert
 consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
 want to evaluateDefine Positive Levels: Specify what constitutes a
 "positive" result for each testConfigure Options: Enable statistical
 comparisons, confidence intervals, or visualizations as neededRun
 Analysis: Review results tables and clinical interpretationsCopy
 Report: Use the auto-generated sentences for your documentation<div
 style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
 margin-top: 0;"> Key Metrics Explained

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #6a1b9a;">

 Sensitivity: Probability test is positive when disease present
 (rule-out ability)

 Specificity: Probability test is negative when disease absent (rule-in
 ability)

 PPV: Probability of disease when test positive

 NPV: Probability of no disease when test negative

 LR+: How much positive test increases odds of disease

 LR-: How much negative test decreases odds of disease

 Accuracy: Overall probability of correct classification

 McNemar Test: Statistical comparison between paired tests

 <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines

 <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
 color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
 (Rule-Out):

 <p style="margin-top: 0;">• Sensitivity ≥95%: Excellent
 • NPV ≥95%: High confidence
 • Goal: Minimize false negatives

 <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):

 <p style="margin-top: 0;">• Specificity ≥95%: Excellent
 • PPV ≥90%: High confidence
 • Goal: Minimize false positives

 <div style="background-color: #fff8e1; border: 1px solid #ffc107;
 padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
 #f57f17; margin-top: 0;"> Important Assumptions & Limitations

 <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
 your reference test is truly accurateSample Size: Results more
 reliable with larger, representative samplesPrevalence Dependency: PPV
 and NPV vary with disease prevalenceMcNemar Test: Requires
 paired/matched data for statistical comparisonsMissing Data: Cases
 with incomplete data are excluded from analysisConfidence Intervals:
 Calculated using Wilson method for better accuracy

 <div style='margin: 10px 0;'><div style='background-color: #eff6ff;
 border-left: 4px solid #93c5fd; padding: 12px; margin: 8px 0;
 border-radius: 4px;'><strong style='color: #2563eb;'> Analysis
 Completed Successfully
 <span style='color: #374151;'>3 diagnostic tests compared using 180
 complete cases. Gold standard identified 63 diseased and 117 healthy
 cases. Review comparison tables and statistical tests below.

> data(diagnostic_sample_size_examples)

> print(diagnostic_sample_size_examples[, c("scenario", 
+     "prevalence", "final_n", "final_n_adjusted")])
                                      scenario prevalence final_n
1                 Colorectal Cancer Blood Test       0.10     940
2                  COVID-19 Rapid Antigen Test       0.05    1880
3      AI-Based Diabetic Retinopathy Detection       0.30     147
4 Rare Disease Biomarker (e.g., Fabry Disease)       0.02    7900
5                   Lung Cancer LDCT Screening       0.15    4020
6                Digital Mammography Screening       0.50      88
  final_n_adjusted
1             1175
2             2212
3              164
4            10534
5             5025
6              104

> colorectal <- diagnostic_sample_size_examples[1, ]

> cat("Scenario:", colorectal$scenario, "\n")
Scenario: Colorectal Cancer Blood Test 

> cat("Prevalence:", colorectal$prevalence * 100, "%\n")
Prevalence: 10 %

> cat("Required N:", colorectal$final_n, "\n")
Required N: 940 

> cat("Adjusted N (20% non-response):", colorectal$final_n_adjusted, 
+     "\n")
Adjusted N (20% non-response): 1175 

> prevalence_impact <- diagnostic_sample_size_examples[, 
+     c("scenario", "prevalence", "final_n")]

> prevalence_impact <- prevalence_impact[order(prevalence_impact$prevalence), 
+     ]

> print(prevalence_impact)
                                      scenario prevalence final_n
4 Rare Disease Biomarker (e.g., Fabry Disease)       0.02    7900
2                  COVID-19 Rapid Antigen Test       0.05    1880
1                 Colorectal Cancer Blood Test       0.10     940
5                   Lung Cancer LDCT Screening       0.15    4020
3      AI-Based Diabetic Retinopathy Detection       0.30     147
6                Digital Mammography Screening       0.50      88

> data(diagnosticmeta_test)

> str(diagnosticmeta_test)
tibble [20 × 7] (S3: tbl_df/tbl/data.frame)
 $ study          : chr [1:20] "Study_1" "Study_2" "Study_3" "Study_4" ...
 $ true_positives : num [1:20] 81 56 84 23 36 44 19 75 65 58 ...
 $ false_positives: num [1:20] 10 6 4 13 32 28 3 6 18 14 ...
 $ false_negatives: num [1:20] 30 6 11 5 20 10 1 6 21 11 ...
 $ true_negatives : num [1:20] 153 120 100 98 95 76 27 105 67 93 ...
 $ year           : int [1:20] 2015 2019 2015 2023 2024 2018 2016 2024 2015 2022 ...
 $ quality_score  : num [1:20] 5 7 7 8 7 3 3 9 7 4 ...

> result <- diagnosticmeta(data = diagnosticmeta_test, 
+     study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+     false_negatives = "false_negatives", true_negatives = "true_negatives", 
+     covariate = NULL)

> result_year <- diagnosticmeta(data = diagnosticmeta_test, 
+     study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+     false_negatives = "false_negatives", true_negatives = "true_negatives", 
+     covariate = "year", meta_regression = TRUE)

> data(diagnosticmeta_test_small)

> result_small <- diagnosticmeta(data = diagnosticmeta_test_small, 
+     study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+     false_negatives = "false_negatives", true_negatives = "true_negatives", 
+     covariate = NULL)

> data(diagnosticmeta_test_categorical)

> result_categorical <- diagnosticmeta(data = diagnosticmeta_test_categorical, 
+     study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+     false_negatives = "false_negatives", true_negatives = "true_negatives", 
+     covariate = "imaging_modality", meta_regression = TRUE)

> data(diagnosticmeta_test_zeros)

> result_none <- diagnosticmeta(data = diagnosticmeta_test_zeros, 
+     study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+     false_negatives = "false_negatives", true_negatives = "true_negatives", 
+     covariate = NULL, zero_cell_correction = "none")

> result_constant <- diagnosticmeta(data = diagnosticmeta_test_zeros, 
+     study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+     false_negatives = "false_negatives", true_negatives = "true_negatives", 
+     covariate = NULL, zero_cell_correction = "constant")

> data(diagnosticmeta_test_large)

> system.time({
+     result_large <- diagnosticmeta(data = diagnosticmeta_test_large, 
+         study = "study", true_positives = "true_positives", false_positives = "false_positives", 
+         false_negatives = "false_negatives", true_negatives = "true_negatives", 
+         covariate = NULL, bivariate_analysis = TRUE, heterogeneity_analysis = TRUE)
+ })
   user  system elapsed 
  0.114   0.013   0.133 

> data(groomecompare_test)

> str(groomecompare_test)
tibble [150 × 7] (S3: tbl_df/tbl/data.frame)
 $ patient_id: chr [1:150] "PT001" "PT002" "PT003" "PT004" ...
 $ time      : num [1:150] 7.1 10.5 6.5 0.5 4.1 ...
 $ event     : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 1 1 1 ...
 $ ypTNM     : Ord.factor w/ 4 levels "Stage I"<"Stage II"<..: 2 1 4 1 3 1 2 3 1 1 ...
 $ RPA       : Ord.factor w/ 3 levels "Low Risk"<"Intermediate"<..: 1 1 1 1 2 1 1 1 2 2 ...
 $ age       : num [1:150] 47 52 78 61 69 65 71 54 60 43 ...
 $ sex       : Factor w/ 2 levels "Female","Male": 1 2 2 1 1 1 2 1 2 2 ...

> summary(groomecompare_test)
  patient_id             time         event        ypTNM              RPA    
 Length:150         Min.   :  0.200   0:77   Stage I  :38   Low Risk    :59  
 Class :character   1st Qu.:  3.325   1:73   Stage II :47   Intermediate:55  
 Mode  :character   Median :  6.650          Stage III:45   High Risk   :36  
                    Mean   : 10.839          Stage IV :20                    
                    3rd Qu.: 14.275                                          
                    Max.   :102.400                                          
      age            sex    
 Min.   :35.00   Female:62  
 1st Qu.:58.00   Male  :88  
 Median :65.00              
 Mean   :64.82              
 3rd Qu.:72.00              
 Max.   :92.00              

> table(groomecompare_test$event)

 0  1 
77 73 

> prop.table(table(groomecompare_test$event))

        0         1 
0.5133333 0.4866667 

> table(groomecompare_test$ypTNM)

  Stage I  Stage II Stage III  Stage IV 
       38        47        45        20 

> table(groomecompare_test$RPA)

    Low Risk Intermediate    High Risk 
          59           55           36 

> table(groomecompare_test$ypTNM, groomecompare_test$RPA)
           
            Low Risk Intermediate High Risk
  Stage I         17           12         9
  Stage II        22           14        11
  Stage III       13           21        11
  Stage IV         7            8         5

> data <- data.frame(SampleID = paste0("S", 1:10), TP53 = c(1, 
+     0, 1, 0, 1, 0, 0, 1, 0, 1), KRAS = c(0, 1, 0, 1, 0, 1, 1, 
+     0, 1, 0))

> jjoncoplot(data, "SampleID", c("TP53", "KRAS"))
Error in item$visible && !is(item, "Image") : 
  invalid 'x' type in 'x && y'
Calls: <Anonymous> ... print -> print.R6 -> <Anonymous> -> cat -> <Anonymous>
In addition: There were 50 or more warnings (use warnings() to see the first 50)
ℹ Loading ClinicoPath
Serdar Balci MD Pathologist 
 https://www.serdarbalci.com/ 
 
Execution halted
There were 50 or more warnings (use warnings() to see the first 50)