Development and Validation of Risk Prediction Models

Damien Drubay (), Ben Van Calster () and Stefan Michiels ()
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Damien Drubay: INSERM U1018, CESP, Paris-Saclay University, UVSQ
Ben Van Calster: KU Leuven, Department of Development and Regeneration
Stefan Michiels: INSERM U1018, CESP, Paris-Saclay University, UVSQ

Chapter 101 in Principles and Practice of Clinical Trials, 2022, pp 2003-2024 from Springer

Abstract: Abstract 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.

Keywords: Prediction models; Diagnostic; Prognostic; Treatment effect; Precision medicine; Development; Predictors; Validation; Calibration; Discrimination; Utility (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-52636-2_138

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DOI: 10.1007/978-3-319-52636-2_138

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