Diagnosis (Berl). 2026 Mar 2. doi: 10.1515/dx-2025-0152. Online ahead of print.
ABSTRACT
OBJECTIVES: Clinical prediction requires formalizing uncertainty into a statistical model. However, persistent confusion between prediction and inference, and between traditional (stepwise) and modern (penalized) development strategies, leads to unstable, poorly calibrated, and overfit models. A structured statistical framework is essential.
METHODS: This article is a structured, didactic tutorial that explains the core concepts of clinical prediction models. It covers the definition of a prediction model, the fundamental strategies for its construction, and the essential framework for its evaluation, illustrated through an applied example using real-world clinical data.
RESULTS: The tutorial illustrates model development using the GUSTO-I dataset (N = 40,830). Penalized methods (LASSO and Elastic Net) successfully identified clinical signals while eliminating engineered noise variables. The LASSO model (λ1se) achieved excellent discrimination (AUC 0.818; 95 % CI: 0.803-0.832) and overall accuracy (Brier score 0.058). Calibration analysis revealed a slope of 1.28 and intercept of 0.63, identifying conservative bias and systematic risk underestimation inherent to λ1se selection. Decision curve analysis confirmed significant clinical utility across relevant probability thresholds.
CONCLUSIONS: This guide equips clinicians with a rigorous methodological framework for the critical appraisal and interpretation of modern clinical prediction models.
PMID:41762231 | DOI:10.1515/dx-2025-0152
