Prediction Models
Everything on Aqrab tagged Prediction Models — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
Predicted Treatment Benefit: When a Risk Model Is Not a Treatment Recommendation
A practical guide to separating predicted outcome risk from predicted treatment benefit. Learn why a high-risk patient is not automatically a high-benefit patient, how risk modeling and effect modeling differ, and what reviewers should demand before trusting a personalized treatment claim.
The Imperfect Gold Standard: Measuring a Test When the Reference Is Wrong Too
A practical guide to imperfect reference standards and latent class analysis for clinical researchers. Covers why apparent sensitivity and specificity are biased when the gold standard is itself flawed, why conditional dependence between tests flips the bias from pessimistic to optimistic, when latent class analysis helps and when it fails, and what reviewers should demand.
Incorporation Bias: When a Test Helps Write Its Own Answer Key
A practical guide to incorporation bias for clinical researchers. Covers why sensitivity and specificity both inflate toward 100% when the index test is used to define the reference standard, how it differs from verification and spectrum bias, why there is no clean statistical correction, and what reviewers should demand before trusting a diagnostic accuracy.
Verification Bias: When the Test Under Study Decides Who Gets the Gold Standard
A practical guide to verification bias (workup bias) for clinical researchers. Covers why sensitivity is inflated and specificity deflated when the index test drives who gets the reference standard, the Begg-Greenes correction, differential verification, and what reviewers should demand before trusting a diagnostic accuracy.
Spectrum Bias: Why a Test’s Accuracy Is Not a Property of the Test
A practical guide to spectrum bias for clinical researchers. Covers why sensitivity and specificity shift with the case-mix of who was enrolled, the two-gate case-control trap, how curated data inflates AI-diagnostic performance, and what reviewers should demand before trusting a reported accuracy.
PROBAST: When a Prediction Model Paper Looks Ready Before It Earns Trust
A practical guide to PROBAST for clinical researchers. Covers participant selection, predictor leakage, outcome definition, overfitting, calibration, and what reviewers should demand before trusting a clinical prediction model.
Calibration Drift: When a Good Model Keeps the Right Rank and Still Gives the Wrong Risk
A practical guide to calibration drift for clinical researchers. Covers baseline-risk shift, calibration slope failure, threshold consequences, and what reviewers should demand before trusting deployment-ready prediction claims.
Data Leakage in Clinical Prediction Models: When the Model Learns the Future
A practical guide to data leakage in clinical prediction models for clinical researchers. Covers post-outcome features, workflow proxies, validation traps, and what reviewers should demand before trusting a headline AUC.
Net Reclassification Improvement: When a New Biomarker Wins by Moving Patients Between the Wrong Boxes
A practical guide to net reclassification improvement for clinical researchers. Covers event and non-event NRI, arbitrary risk categories, overtreatment traps, and what reviewers should demand before trusting claims that a new model improved classification.
Decision Curve Analysis: When a Better AUC Still Makes Worse Clinical Decisions
A practical guide to decision curve analysis for clinical researchers. Covers net benefit, threshold probability, when prediction models fail to beat treat-all or treat-none strategies, and what reviewers should demand before trusting claims of clinical utility.
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