nogoldstandard Screening Data - Five-Test Panel
Source:R/data_nogoldstandard_docs.R
nogoldstandard_screening.RdComprehensive five-test screening dataset with 250 patients. Tests include imaging, clinical exam, biomarker, questionnaire, and AI algorithm with varying characteristics (Sens: 0.82-0.60, Spec: 0.92-0.75).
Format
A data frame with 250 rows and 8 variables:
- patient_id
Character: Patient identifier (PT001-PT250)
- Imaging
Factor: Imaging result ("Normal", "Abnormal"), Sens=0.82, Spec=0.90
- ClinicalExam
Factor: Clinical exam ("Normal", "Abnormal"), Sens=0.65, Spec=0.85
- Biomarker
Factor: Biomarker test ("Normal", "Abnormal"), Sens=0.70, Spec=0.88
- Questionnaire
Factor: Risk questionnaire ("Negative", "Positive"), Sens=0.60, Spec=0.75
- AI_Algorithm
Factor: AI prediction ("Negative", "Positive"), Sens=0.88, Spec=0.92
- age
Numeric: Patient age in years (mean 58, SD 15)
- screening_round
Numeric: Screening round number (1-5)
Details
Simulated with 15% disease prevalence (screening setting). Five tests with diverse characteristics demonstrate comprehensive evaluation methods.
Examples
data(nogoldstandard_screening)
nogoldstandard(data = nogoldstandard_screening,
test1 = "Imaging", test1Positive = "Abnormal",
test2 = "ClinicalExam", test2Positive = "Abnormal",
test3 = "Biomarker", test3Positive = "Abnormal",
test4 = "Questionnaire", test4Positive = "Positive",
test5 = "AI_Algorithm", test5Positive = "Positive",
clinicalPreset = "screening_evaluation")
#>
#> ANALYSIS WITHOUT GOLD STANDARD
#> WARNING: Clinical preset: screening evaluation
#> Designed for population screening test evaluation Use for evaluating screening programs with multiple tests This preset does NOT change your settings automatically -- set them yourself in the options panel: Analysis method: currently "latent_class", recommended "any_positive"; Bootstrap confidence intervals: currently off, recommended on; Bootstrap samples: currently 1000, recommended 500.
#>
#> Analysing 250 cases
#> All 250 cases have a result for every selected test.
#> Agreement Statistics (Cohen's Kappa)
#> ────────────────────────────────────────────────────────────────────────
#> Test Pair Kappa p-value Agreement
#> ────────────────────────────────────────────────────────────────────────
#> Imaging vs ClinicalExam 0.3750000 0.0000003 77.60000
#> Imaging vs Biomarker 0.3672457 0.0000034 79.60000
#> Imaging vs Questionnaire 0.0000000 NaN 0.00000
#> Imaging vs AI_Algorithm 0.0000000 NaN 0.00000
#> ClinicalExam vs Biomarker 0.2783551 0.0003003 74.00000
#> ClinicalExam vs Questionnaire 0.0000000 NaN 0.00000
#> ClinicalExam vs AI_Algorithm 0.0000000 NaN 0.00000
#> Biomarker vs Questionnaire 0.0000000 NaN 0.00000
#> Biomarker vs AI_Algorithm 0.0000000 NaN 0.00000
#> Questionnaire vs AI_Algorithm 0.2058590 0.0091408 71.20000
#> ────────────────────────────────────────────────────────────────────────
#> Note. Kappa standard errors and p-values use a large-sample normal
#> approximation rather than the exact asymptotic SE (e.g.
#> vcd::Kappa); interpret p-values cautiously, especially in small
#> samples.
#>
#>
#> <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 latent_class method
#>
#> Tests analyzed: Imaging, ClinicalExam, Biomarker, Questionnaire,
#> AI_Algorithm (N=5)
#>
#> Disease prevalence: 18.1%
#>
#> Test sensitivities: Range from 53.2% to 87.2%
#>
#> 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: The only method here that estimates accuracy rather than
#> agreement with a self-built reference; provides model fit statistics.
#> Assumes the tests are conditionally independent given true status --
#> it does NOT model conditional dependence
#>
#> <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. Not an accuracy estimate: each test
#> helps build the standard it is judged against, which inflates its
#> apparent performance. Needs 3+ tests -- with 2 a tie counts as
#> diseased, making it identical to Any Test Positive
#>
#> <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: A deliberately strict reference. Sensitivity and NPV cannot
#> be estimated under this rule -- they are fixed at 100% by construction
#> -- so only specificity and PPV are shown, and both are inflated by the
#> same circularity
#>
#> <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: A deliberately permissive reference. Specificity and PPV
#> cannot be estimated under this rule -- they are fixed at 100% by
#> construction -- so only sensitivity and NPV are shown, and both are
#> inflated by the same circularity
#>
#> <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
#> ───────────────────────────────────────
#> 18.14302 13.36596 22.92008
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Imaging 81.75046 70.50974 92.99119 93.68655 90.35442 97.01868 74.16001 95.86123
#> ClinicalExam 76.51191 64.17485 88.84897 84.70695 79.77570 89.63820 52.58170 94.20999
#> Biomarker 64.29725 50.35380 78.24071 89.32951 85.09952 93.55951 57.18364 91.86237
#> Questionnaire 53.17036 38.64863 67.69210 79.04480 73.46868 84.62093 35.99518 88.39302
#> AI_Algorithm 87.20045 77.47791 96.92300 94.40585 91.25725 97.55444 77.55291 97.08263
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. 95% intervals are normal-approximation (Wald) intervals, using the estimated number of diseased cases as the
#> denominator for sensitivity and non-diseased for specificity. They treat the estimates as observed proportions and so
#> understate the uncertainty of a latent-variable model; enable Bootstrap for intervals that account for the estimation
#> itself.
#>
#>
#> Model Fit Statistics
#> ────────────────────────────────────
#> Statistic Value
#> ────────────────────────────────────
#> BIC 1218.82552
#> AIC 1180.08945
#> Log-Likelihood -579.04473
#> G-squared 14.60707
#> Chi-squared 11.77809
#> Degrees of Freedom 20.00000
#> ────────────────────────────────────
#>
#>
#> Conditional Independence Check (Bivariate Residuals)
#> ───────────────────────────────────────────────────────────────────────────────────────
#> Test Pair Bivariate Residual Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────
#> Imaging vs ClinicalExam 0.15185514 Consistent with independence
#> Imaging vs Biomarker 0.14793587 Consistent with independence
#> Imaging vs Questionnaire 0.35557163 Consistent with independence
#> Imaging vs AI_Algorithm 0.40176613 Consistent with independence
#> ClinicalExam vs Biomarker 0.13339412 Consistent with independence
#> ClinicalExam vs Questionnaire 0.10335599 Consistent with independence
#> ClinicalExam vs AI_Algorithm 0.06225217 Consistent with independence
#> Biomarker vs Questionnaire 0.43600437 Consistent with independence
#> Biomarker vs AI_Algorithm 0.16860161 Consistent with independence
#> Questionnaire vs AI_Algorithm 0.06687499 Consistent with independence
#> ───────────────────────────────────────────────────────────────────────────────────────
#> Note. A residual above 3.84 (the 5% point of chi-squared on 1 degree of freedom)
#> is evidence that the pair does not err independently. This is a descriptive
#> check, not a formal test: the residuals are correlated with one another and no
#> multiplicity adjustment is applied.
#>
#>
#> Test Cross-Tabulation
#> ─────────────────────────────────────────────────────────────────────────────────────────────
#> Test Combination Count Percentage
#> ─────────────────────────────────────────────────────────────────────────────────────────────
#> Imaging-, ClinicalExam-, Biomarker-, Questionnaire-, AI_Algorithm- 108 43.20000
#> Imaging-, ClinicalExam-, Biomarker-, Questionnaire+, AI_Algorithm- 30 12.00000
#> Imaging-, ClinicalExam+, Biomarker-, Questionnaire-, AI_Algorithm- 18 7.20000
#> Imaging-, ClinicalExam-, Biomarker+, Questionnaire-, AI_Algorithm- 13 5.20000
#> Imaging+, ClinicalExam+, Biomarker+, Questionnaire+, AI_Algorithm+ 11 4.40000
#> Imaging+, ClinicalExam-, Biomarker-, Questionnaire-, AI_Algorithm- 8 3.20000
#> Imaging-, ClinicalExam-, Biomarker-, Questionnaire-, AI_Algorithm+ 7 2.80000
#> Imaging-, ClinicalExam+, Biomarker-, Questionnaire+, AI_Algorithm- 5 2.00000
#> Imaging+, ClinicalExam+, Biomarker-, Questionnaire-, AI_Algorithm+ 5 2.00000
#> Imaging-, ClinicalExam-, Biomarker+, Questionnaire+, AI_Algorithm- 4 1.60000
#> Imaging-, ClinicalExam+, Biomarker-, Questionnaire-, AI_Algorithm+ 4 1.60000
#> Imaging+, ClinicalExam+, Biomarker+, Questionnaire-, AI_Algorithm+ 4 1.60000
#> Imaging+, ClinicalExam+, Biomarker-, Questionnaire+, AI_Algorithm+ 4 1.60000
#> Imaging+, ClinicalExam+, Biomarker-, Questionnaire-, AI_Algorithm- 3 1.20000
#> Imaging-, ClinicalExam+, Biomarker+, Questionnaire-, AI_Algorithm- 3 1.20000
#> Imaging+, ClinicalExam-, Biomarker+, Questionnaire-, AI_Algorithm+ 3 1.20000
#> Imaging-, ClinicalExam+, Biomarker+, Questionnaire-, AI_Algorithm+ 3 1.20000
#> Imaging+, ClinicalExam-, Biomarker+, Questionnaire+, AI_Algorithm+ 3 1.20000
#> Imaging+, ClinicalExam+, Biomarker+, Questionnaire-, AI_Algorithm- 2 0.80000
#> Imaging+, ClinicalExam-, Biomarker-, Questionnaire+, AI_Algorithm- 2 0.80000
#> Imaging+, ClinicalExam-, Biomarker-, Questionnaire+, AI_Algorithm+ 2 0.80000
#> Imaging+, ClinicalExam-, Biomarker+, Questionnaire+, AI_Algorithm- 1 0.40000
#> Imaging-, ClinicalExam+, Biomarker+, Questionnaire+, AI_Algorithm- 1 0.40000
#> Imaging+, ClinicalExam+, Biomarker+, Questionnaire+, AI_Algorithm- 1 0.40000
#> Imaging+, ClinicalExam-, Biomarker-, Questionnaire-, AI_Algorithm+ 1 0.40000
#> Imaging-, ClinicalExam-, Biomarker+, Questionnaire-, AI_Algorithm+ 1 0.40000
#> Imaging-, ClinicalExam-, Biomarker-, Questionnaire+, AI_Algorithm+ 1 0.40000
#> Imaging-, ClinicalExam+, Biomarker-, Questionnaire+, AI_Algorithm+ 1 0.40000
#> Imaging-, ClinicalExam+, Biomarker+, Questionnaire+, AI_Algorithm+ 1 0.40000
#> Imaging+, ClinicalExam-, Biomarker+, Questionnaire-, AI_Algorithm- 0 0.00000
#> Imaging+, ClinicalExam+, Biomarker-, Questionnaire+, AI_Algorithm- 0 0.00000
#> Imaging-, ClinicalExam-, Biomarker+, Questionnaire+, AI_Algorithm+ 0 0.00000
#> ─────────────────────────────────────────────────────────────────────────────────────────────
#>
