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
#> WARNING: Clinical preset: diagnostic validation
#> Recommended for validating new diagnostic tests against existing standards Use when evaluating new biomarkers or diagnostic technologies This preset does NOT change your settings automatically -- set them yourself in the options panel: Bootstrap confidence intervals: currently off, recommended on.
#>
#> Analysing 190 cases
#> All 190 cases have a result for every selected test.
#> 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
#> ────────────────────────────────────────────────────────────────────
#> 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: New_Test, Reference1, Reference2 (N=3)
#>
#> Disease prevalence: 34.5%
#>
#> Test sensitivities: Range from 83.4% to 90.9%
#>
#> 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: 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
#> ───────────────────────────────────────
#> 34.46658 27.70884 41.22433
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> New_Test 88.50060 80.77409 96.22710 92.73649 88.17781 97.29516 86.50141 93.87760
#> Reference1 90.85540 83.87421 97.83660 85.14059 78.89305 91.38813 76.27952 94.65315
#> Reference2 83.36826 74.34961 92.38691 92.44657 87.80508 97.08806 85.30462 91.35592
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 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 624.352992
#> AIC 601.623823
#> Log-Likelihood -293.811912
#> Degrees of Freedom 0.000000
#> ─────────────────────────────────────
#> Note. With three tests this
#> model has as many parameters as
#> the data can support (0
#> residual degrees of freedom),
#> so it reproduces the observed
#> table exactly. Goodness-of-fit
#> statistics are therefore
#> omitted: they cannot tell you
#> whether the
#> conditional-independence
#> assumption holds. Use four or
#> more tests if you need to test
#> the model's fit.
#>
#>
#> Conditional Independence Check (Bivariate Residuals)
#> ─────────────────────────────────────────────────────
#> Test Pair Bivariate Residual Interpretation
#> ─────────────────────────────────────────────────────
#> ─────────────────────────────────────────────────────
#> Note. Not computable with three tests: the
#> model has no residual degrees of freedom, so it
#> reproduces every observed table exactly and no
#> residual can detect conditional dependence. Add
#> a fourth test if you need to check this
#> assumption.
#>
#>
#> 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
#> ──────────────────────────────────────────────────────────────
#>
