Developing clinical prediction models: a step-by-step guide

By O. Efthimiou, Michael Seo, K. Chalkou, T. Debray, Matthias Egger, Georgia Salanti. British medical journal, 2024

337 citations recorded by the source corpus.

Predicting future outcomes of patients is essential to clinical practice, with many prediction models published each year. Empirical evidence suggests that published studies often have severe methodological limitations, which undermine their usefulness. This article presents a step-by-step guide to help researchers develop and evaluate a clinical prediction model. The guide covers best practices in defining the aim and users, selecting data sources, addressing missing data, exploring alternative modelling options, and assessing model performance. The steps are illustrated using an example from relapsing-remitting multiple sclerosis. Comprehensive R code is also provided.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a1fec9850e7bbdbdaad0b93e9eac7c4ed70f04c8