Key Steps and Common Pitfalls in Clinical Prediction Model Research

Clinical prediction models estimate an individual’s risk of a particular health outcome. Thousands of prediction models are published each year, yet few are reliable or fit for purpose. In this talk, I outline key steps and common pitfalls in prediction model research, and outline ways to produce more reliable and clinically useful models - including protocols, better handling of continuous predictors, examining calibration and clinical utility, checking model (in)stability, and ensuring sample size is appropriate for model development. Thanks for watching!