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Development and Validation of Risk Prediction Models

In: Principles and Practice of Clinical Trials

Author

Listed:
  • Damien Drubay

    (INSERM U1018, CESP, Paris-Saclay University, UVSQ
    Service de Biostatistique et d’Epidémiologie, Gustave Roussy)

  • Ben Van Calster

    (KU Leuven, Department of Development and Regeneration
    Leiden University Medical Center, Department of Biomedical Data Sciences)

  • Stefan Michiels

    (INSERM U1018, CESP, Paris-Saclay University, UVSQ
    Service de Biostatistique et d’Epidémiologie, Gustave Roussy)

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.

Suggested Citation

  • Damien Drubay & Ben Van Calster & Stefan Michiels, 2022. "Development and Validation of Risk Prediction Models," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 101, pages 2003-2024, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_138
    DOI: 10.1007/978-3-319-52636-2_138
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