Development and Validation of Risk Prediction Models

There has been increased interest in the use of clinical risk prediction models for decision-making in medicine for patient care. This has been accelerated through the focus on precision medicine, the revolution in omics data, and increasing use of randomized controlled trial and electronic health record databases. These models are expected to assist diagnostic assessment, prognostication, and therapeutic decision-making. Randomized controlled trial data are highly relevant for modeling treatment benefit and treatment effect heterogeneity. The development and validation of prediction models requires careful methodology and reporting, and an evidence-based approach is needed to bring risk prediction models to clinical practice. This chapter provides an overview of the key steps and considerations to develop and validate risk prediction models. We comment on the role of clinical trials throughout the process. A risk prediction model for the occurrence of breast cancer is used as an example.