nogoldstandard Pathology Data - Inter-Pathologist Agreement
Source:R/data_nogoldstandard_docs.R
nogoldstandard_pathology.RdThree-pathologist dataset with 180 patients for assessing diagnostic agreement without a gold standard. Pathologists have varying sensitivity (0.88, 0.85, 0.82) and high specificity (0.92, 0.90, 0.93).
Format
A data frame with 180 rows and 6 variables:
- patient_id
Character: Patient identifier (PT001-PT180)
- Pathologist1
Factor: First pathologist diagnosis ("Benign", "Malignant"), Sens=0.88, Spec=0.92
- Pathologist2
Factor: Second pathologist diagnosis ("Benign", "Malignant"), Sens=0.85, Spec=0.90
- Pathologist3
Factor: Third pathologist diagnosis ("Benign", "Malignant"), Sens=0.82, Spec=0.93
- tumor_site
Factor: Tumor location (Lung, Breast, Colon, Prostate)
- specimen_quality
Factor: Specimen quality (Adequate, Limited, Poor)
Details
Simulated with 25% malignancy prevalence. Pathologists show realistic variation in diagnostic accuracy. Ideal for pathology agreement studies using latent class analysis.
Examples
data(nogoldstandard_pathology)
nogoldstandard(data = nogoldstandard_pathology,
test1 = "Pathologist1", test1Positive = "Malignant",
test2 = "Pathologist2", test2Positive = "Malignant",
test3 = "Pathologist3", test3Positive = "Malignant",
test4Positive = "", test5Positive = "",
clinicalPreset = "pathology_agreement")
#>
#> ANALYSIS WITHOUT GOLD STANDARD
#> WARNING: Clinical preset: pathology agreement
#> Optimal for assessing agreement between pathologists or observers Use for inter-rater reliability studies in pathology This preset does NOT change your settings automatically -- set them yourself in the options panel: Analysis method: currently "latent_class", recommended "composite"; Bootstrap samples: currently 1000, recommended 500.
#>
#> Analysing 180 cases
#> All 180 cases have a result for every selected test.
#> Agreement Statistics (Cohen's Kappa)
#> ────────────────────────────────────────────────────────────────────────
#> Test Pair Kappa p-value Agreement
#> ────────────────────────────────────────────────────────────────────────
#> Pathologist1 vs Pathologist2 0.4633028 < .0000001 78.33333
#> Pathologist1 vs Pathologist3 0.6025237 < .0000001 84.44444
#> Pathologist2 vs Pathologist3 0.5065789 < .0000001 80.55556
#> ────────────────────────────────────────────────────────────────────────
#> 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: Pathologist1, Pathologist2, Pathologist3 (N=3)
#>
#> Disease prevalence: 30.4%
#>
#> Test sensitivities: Range from 70.9% to 79.2%
#>
#> 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
#> ───────────────────────────────────────
#> 30.44824 23.72549 37.17099
#> ───────────────────────────────────────
#>
#>
#> Test Performance Metrics
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Lower CI Upper CI Specificity Lower CI Upper CI PPV NPV
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Pathologist1 78.01015 67.04492 88.97537 94.21285 90.12265 98.30306 85.50978 90.72928
#> Pathologist2 70.85991 58.82961 82.89021 90.28387 85.09576 95.47198 76.14915 87.61956
#> Pathologist3 79.16987 68.41868 89.92106 97.91562 95.41313 100.00000 94.32716 91.48035
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 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 553.066616
#> AIC 530.715918
#> Log-Likelihood -258.357959
#> 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
#> ──────────────────────────────────────────────────────────────────────
#> Pathologist1-, Pathologist2-, Pathologist3- 105 58.33333
#> Pathologist1+, Pathologist2+, Pathologist3+ 24 13.33333
#> Pathologist1-, Pathologist2+, Pathologist3- 13 7.22222
#> Pathologist1+, Pathologist2-, Pathologist3+ 10 5.55556
#> Pathologist1+, Pathologist2-, Pathologist3- 9 5.00000
#> Pathologist1+, Pathologist2+, Pathologist3- 7 3.88889
#> Pathologist1-, Pathologist2+, Pathologist3+ 7 3.88889
#> Pathologist1-, Pathologist2-, Pathologist3+ 5 2.77778
#> ──────────────────────────────────────────────────────────────────────
#>
