nogoldstandard Diagnostic Validation Data
Source:R/data_nogoldstandard_docs.R
nogoldstandard_validation.RdDataset with 190 patients for validating a new diagnostic test against two reference tests without a gold standard. Tests have good characteristics (Sens: 0.88-0.82, Spec: 0.90-0.88).
Format
A data frame with 190 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT190)
- New_Test
Factor: Test being validated ("Negative", "Positive"), Sens=0.88, Spec=0.90
- Reference1
Factor: First reference test ("Negative", "Positive"), Sens=0.85, Spec=0.88
- Reference2
Factor: Second reference test ("Negative", "Positive"), Sens=0.82, Spec=0.92
- test_site
Factor: Testing site (Academic, Community, Private)
Details
Simulated with 32% prevalence. Designed for diagnostic test validation studies using latent class or Bayesian methods.
Examples
data(nogoldstandard_validation)
nogoldstandard(data = nogoldstandard_validation,
test1 = "New_Test", test1Positive = "Positive",
test2 = "Reference1", test2Positive = "Positive",
test3 = "Reference2", test3Positive = "Positive",
test4Positive = "", test5Positive = "",
clinicalPreset = "diagnostic_validation")
#>
#> ANALYSIS WITHOUT GOLD STANDARD
#>
#> Agreement Statistics (Cohen's Kappa)
#> ────────────────────────────────────────────────────────────────────
#> Test Pair Kappa p-value Agreement
#> ────────────────────────────────────────────────────────────────────
#> New_Test vs Reference1 0.5888408 < .0000001 80.52632
#> New_Test vs Reference2 0.6156675 < .0000001 82.63158
#> Reference1 vs Reference2 0.5528360 < .0000001 78.94737
#> ────────────────────────────────────────────────────────────────────
#>
#>
#> <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: New_Test, Reference1, Reference2 (N=3)
#>
#> Disease prevalence: 23.2%
#>
#> 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
#> ───────────────────────────────────────
#> 23.15789 17.15970 29.15609
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> New_Test 100.00000 100.00000 100.00000 84.24658 79.06651 89.42664 65.67164 100.00000
#> Reference1 100.00000 100.00000 100.00000 76.71233 70.70243 82.72222 56.41026 100.00000
#> Reference2 100.00000 100.00000 100.00000 86.30137 81.41238 91.19035 68.75000 100.00000
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Test Cross-Tabulation
#> ──────────────────────────────────────────────────────────────
#> Test Combination Count Percentage
#> ──────────────────────────────────────────────────────────────
#> New_Test-, Reference1-, Reference2- 91 47.89474
#> New_Test+, Reference1+, Reference2+ 44 23.15789
#> New_Test-, Reference1+, Reference2- 17 8.94737
#> New_Test+, Reference1+, Reference2- 10 5.26316
#> New_Test+, Reference1-, Reference2- 8 4.21053
#> New_Test-, Reference1-, Reference2+ 8 4.21053
#> New_Test-, Reference1+, Reference2+ 7 3.68421
#> New_Test+, Reference1-, Reference2+ 5 2.63158
#> ──────────────────────────────────────────────────────────────
#>
