nogoldstandard Pathology Data - Inter-Pathologist Agreement
Source:R/data_nogoldstandard_docs.R
nogoldstandard_pathology.RdThree-pathologist dataset with 180 patients for assessing diagnostic agreement without a gold standard. Pathologists have varying sensitivity (0.88, 0.85, 0.82) and high specificity (0.92, 0.90, 0.93).
Format
A data frame with 180 rows and 6 variables:
- patient_id
Character: Patient identifier (PT001-PT180)
- Pathologist1
Factor: First pathologist diagnosis ("Benign", "Malignant"), Sens=0.88, Spec=0.92
- Pathologist2
Factor: Second pathologist diagnosis ("Benign", "Malignant"), Sens=0.85, Spec=0.90
- Pathologist3
Factor: Third pathologist diagnosis ("Benign", "Malignant"), Sens=0.82, Spec=0.93
- tumor_site
Factor: Tumor location (Lung, Breast, Colon, Prostate)
- specimen_quality
Factor: Specimen quality (Adequate, Limited, Poor)
Details
Simulated with 25% malignancy prevalence. Pathologists show realistic variation in diagnostic accuracy. Ideal for pathology agreement studies using latent class analysis.
Examples
data(nogoldstandard_pathology)
nogoldstandard(data = nogoldstandard_pathology,
test1 = "Pathologist1", test1Positive = "Malignant",
test2 = "Pathologist2", test2Positive = "Malignant",
test3 = "Pathologist3", test3Positive = "Malignant",
test4Positive = "", test5Positive = "",
clinicalPreset = "pathology_agreement")
#>
#> ANALYSIS WITHOUT GOLD STANDARD
#>
#> Agreement Statistics (Cohen's Kappa)
#> ────────────────────────────────────────────────────────────────────────
#> Test Pair Kappa p-value Agreement
#> ────────────────────────────────────────────────────────────────────────
#> Pathologist1 vs Pathologist2 0.4633028 < .0000001 78.33333
#> Pathologist1 vs Pathologist3 0.6025237 < .0000001 84.44444
#> Pathologist2 vs Pathologist3 0.5065789 < .0000001 80.55556
#> ────────────────────────────────────────────────────────────────────────
#>
#>
#> <div class='clinical-summary' style='background: #f0f8ff; padding:
#> 15px; border-radius: 8px; margin: 10px 0;'><h4 style='color: #1565c0;
#> margin-top: 0;'> Clinical Summary
#>
#> Analysis: No gold standard analysis using all_positive method
#>
#> Tests analyzed: Pathologist1, Pathologist2, Pathologist3 (N=3)
#>
#> Disease prevalence: 13.3%
#>
#> Test sensitivities: Range from 100.0% to 100.0%
#>
#> Clinical interpretation: Moderate prevalence setting - balanced
#> diagnostic performance
#>
#> <div style='background: #f8f9fa; padding: 20px; border-radius: 8px;
#> margin: 15px 0; border-left: 4px solid #007bff;'><h3 style='color:
#> #007bff; margin-top: 0;'> Method Selection Guide
#>
#> <div style='margin: 15px 0; padding: 15px; background: #e8f5e8;
#> border-radius: 5px;'><h4 style='color: #2e7d32; margin-top: 0;'>
#> Latent Class Analysis (Recommended)
#>
#> Description: Most robust method using mixture models. Estimates
#> disease prevalence and test parameters simultaneously.
#>
#> Best for: Diagnostic validation studies with 3+ tests and N>=100
#>
#> Strengths: Handles conditional dependence, provides model fit
#> statistics, most statistically rigorous
#>
#> <div style='margin: 15px 0; padding: 15px; background: #e3f2fd;
#> border-radius: 5px;'><h4 style='color: #1565c0; margin-top: 0;'>
#> Bayesian Analysis
#>
#> Description: Incorporates prior knowledge about test performance using
#> Bayesian methods.
#>
#> Best for: Studies where you have prior information about expected
#> sensitivity/specificity
#>
#> Strengths: Uses prior knowledge, handles uncertainty well, good for
#> smaller samples
#>
#> <div style='margin: 15px 0; padding: 15px; background: #fff3e0;
#> border-radius: 5px;'><h4 style='color: #ef6c00; margin-top: 0;'>
#> Composite Reference
#>
#> Description: Uses majority vote of available tests as pseudo-gold
#> standard.
#>
#> Best for: Inter-rater agreement studies with 3+ tests, exploratory
#> analysis
#>
#> Strengths: Simple and intuitive, requires minimal assumptions, good
#> starting point
#>
#> <div style='margin: 15px 0; padding: 15px; background: #fce4ec;
#> border-radius: 5px;'><h4 style='color: #c2185b; margin-top: 0;'> All
#> Tests Positive
#>
#> Description: Conservative approach - disease present only if ALL tests
#> are positive.
#>
#> Best for: Highly specific diagnoses where false positives are very
#> costly
#>
#> Strengths: High specificity reference, minimizes false positives
#>
#> <div style='margin: 15px 0; padding: 15px; background: #e8f5e8;
#> border-radius: 5px;'><h4 style='color: #388e3c; margin-top: 0;'> Any
#> Test Positive
#>
#> Description: Liberal approach - disease present if ANY test is
#> positive.
#>
#> Best for: Population screening scenarios where missing cases is costly
#>
#> Strengths: High sensitivity reference, minimizes false negatives
#>
#> <div style='margin: 15px 0; padding: 10px; background: #fff8e1;
#> border-radius: 5px; border-left: 3px solid #ffb300;'><h4 style='color:
#> #e65100; margin-top: 0;'> Selection Tips
#>
#> Start with Latent Class Analysis for most diagnostic studiesUse
#> Composite Reference for quick exploratory analysisChoose All/Any Tests
#> Positive based on clinical consequences of errorsConsider Bayesian if
#> you have strong prior information
#>
#> Disease Prevalence
#> ───────────────────────────────────────
#> Estimate Lower CI Upper CI
#> ───────────────────────────────────────
#> 13.33333 8.36733 18.29934
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Pathologist1 100.00000 100.00000 100.00000 83.33333 77.88899 88.77768 48.00000 100.00000
#> Pathologist2 100.00000 100.00000 100.00000 82.69231 77.16563 88.21898 47.05882 100.00000
#> Pathologist3 100.00000 100.00000 100.00000 85.89744 80.81290 90.98197 52.17391 100.00000
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Test Cross-Tabulation
#> ──────────────────────────────────────────────────────────────────────
#> Test Combination Count Percentage
#> ──────────────────────────────────────────────────────────────────────
#> Pathologist1-, Pathologist2-, Pathologist3- 105 58.33333
#> Pathologist1+, Pathologist2+, Pathologist3+ 24 13.33333
#> Pathologist1-, Pathologist2+, Pathologist3- 13 7.22222
#> Pathologist1+, Pathologist2-, Pathologist3+ 10 5.55556
#> Pathologist1+, Pathologist2-, Pathologist3- 9 5.00000
#> Pathologist1+, Pathologist2+, Pathologist3- 7 3.88889
#> Pathologist1-, Pathologist2+, Pathologist3+ 7 3.88889
#> Pathologist1-, Pathologist2-, Pathologist3+ 5 2.77778
#> ──────────────────────────────────────────────────────────────────────
#>
