Clinical prediction models: from foundational concepts to practical application

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: 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.

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