nogoldstandard Test Data - Basic Two-Test Analysis
Source:R/data_nogoldstandard_docs.R
nogoldstandard_test.RdBasic dataset with 200 patients for analyzing two diagnostic tests without a gold standard reference. Tests have moderate sensitivity (0.85, 0.80) and specificity (0.85, 0.90).
Format
A data frame with 200 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT200)
- Test1
Factor: First test result ("Negative", "Positive"), Sens=0.85, Spec=0.85
- Test2
Factor: Second test result ("Negative", "Positive"), Sens=0.80, Spec=0.90
- age
Numeric: Patient age in years (mean 55, SD 12)
- sex
Factor: "Male" or "Female"
Details
Simulated with latent disease prevalence of 30%. Test characteristics based on realistic diagnostic scenarios. Suitable for demonstrating latent class analysis and composite reference standard methods.
Examples
data(nogoldstandard_test)
nogoldstandard(data = nogoldstandard_test, test1 = "Test1",
test1Positive = "Positive", test2 = "Test2",
test2Positive = "Positive",
test3Positive = "", test4Positive = "",
test5Positive = "", method = "composite")
#>
#> ANALYSIS WITHOUT GOLD STANDARD
#> WARNING: Composite Ties
#> Composite reference with even number of tests may result in ties. Consider using an odd number of tests or a different method.
#> Agreement Statistics (Cohen's Kappa)
#> ──────────────────────────────────────────────────────────
#> Test Pair Kappa p-value Agreement
#> ──────────────────────────────────────────────────────────
#> Test1 vs Test2 0.4651834 < .0000001 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 composite method
#>
#> Tests analyzed: Test1, Test2 (N=2)
#>
#> Disease prevalence: 49.5%
#>
#> Test sensitivities: Range from 69.7% to 79.8%
#>
#> Clinical interpretation: High prevalence setting - high PPV expected,
#> focus on confirming disease
#>
#> <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
#> ───────────────────────────────────────
#> 49.50000 42.57083 56.42917
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test1 79.79798 74.23348 85.36248 100.00000 100.00000 100.00000 100.00000 83.47107
#> Test2 69.69697 63.32780 76.06614 100.00000 100.00000 100.00000 100.00000 77.09924
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Test Cross-Tabulation
#> ───────────────────────────────────────────
#> Test Combination Count Percentage
#> ───────────────────────────────────────────
#> Test1-, Test2- 101 50.50000
#> Test1+, Test2+ 49 24.50000
#> Test1+, Test2- 30 15.00000
#> Test1-, Test2+ 20 10.00000
#> ───────────────────────────────────────────
#>
