Dataset with 150 patients including missing values in test results (~5-8% missingness per test). Three tests with good characteristics.
Format
A data frame with 150 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT150)
- Test1
Factor: First test ("Negative", "Positive"), ~7% missing, Sens=0.85, Spec=0.85
- Test2
Factor: Second test ("Negative", "Positive"), ~5% missing, Sens=0.80, Spec=0.88
- Test3
Factor: Third test ("Negative", "Positive"), ~8% missing, Sens=0.82, Spec=0.90
- age
Numeric: Patient age in years (mean 58, SD 12)
Details
Simulated with 30% prevalence. Missing data introduced randomly to test listwise deletion and missing data handling.
Examples
data(nogoldstandard_missing)
nogoldstandard(data = nogoldstandard_missing,
test1 = "Test1", test1Positive = "Positive",
test2 = "Test2", test2Positive = "Positive",
test3 = "Test3", test3Positive = "Positive",
test4Positive = "", test5Positive = "")
#>
#> ANALYSIS WITHOUT GOLD STANDARD
#>
#> Agreement Statistics (Cohen's Kappa)
#> ──────────────────────────────────────────────────────────
#> Test Pair Kappa p-value Agreement
#> ──────────────────────────────────────────────────────────
#> Test1 vs Test2 0.5472441 < .0000001 80.83333
#> Test1 vs Test3 0.4512195 0.0000012 77.50000
#> Test2 vs Test3 0.3957704 0.0000343 75.00000
#> ──────────────────────────────────────────────────────────
#>
#>
#> <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: Test1, Test2, Test3 (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 7.25124 19.41542
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test1 100.00000 100.00000 100.00000 80.76923 73.71778 87.82069 44.44444 100.00000
#> Test2 100.00000 100.00000 100.00000 79.80769 72.62524 86.99015 43.24324 100.00000
#> Test3 100.00000 100.00000 100.00000 83.65385 77.03765 90.27004 48.48485 100.00000
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Test Cross-Tabulation
#> ─────────────────────────────────────────────────
#> Test Combination Count Percentage
#> ─────────────────────────────────────────────────
#> Test1-, Test2-, Test3- 64 53.33333
#> Test1+, Test2+, Test3+ 16 13.33333
#> Test1+, Test2+, Test3- 9 7.50000
#> Test1-, Test2+, Test3- 8 6.66667
#> Test1-, Test2-, Test3+ 8 6.66667
#> Test1+, Test2-, Test3- 6 5.00000
#> Test1+, Test2-, Test3+ 5 4.16667
#> Test1-, Test2+, Test3+ 4 3.33333
#> ─────────────────────────────────────────────────
#>
