Sample size calculations for clinical prediction model research (aka "goodbye rules of thumb")

Intended for a broad audience, this short talk describes how to calculate the sample size required for developing or validating prediction models in healthcare (also known as prognostic models or predictive algorithms). Currently many researchers use 'rules of thumb' such as 10 events per variable - but here I propose more tailored approaches and illustrate them using applied examples. References are given on the last slide of the talk (and below), and more details are available at www.prognosisresearch.com.
References
DEVELOPMENT
Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part II - binary and time-to-event outcomes. Stat Med. 2019;38(7):1276-96.
Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med. 2019;38(7):1262-75
Riley RD, Ensor J, et al. Calculating the sample size required for developing a clinical prediction model. BMJ 2020;368:m441.
Riley RD, Van Calster B, Collins GS. A note on estimating the Cox-Snell R(2) from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome. Stat Med 2021;40(4):859-64.
VALIDATION
Riley R, Debray TP, Collins GS et al. Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med 2021;40:4230-4251
Archer L, Snell KIE, Ensor J, et al. 2020. Minimum sample size for external validation of a clinical prediction model with a continuous outcome. Stat Med 2021; 40: 133-146
Snell KIE, Archer L, Ensor J, et al. External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. J Clin Epi 2021;135:79-89.

[@junli847]

Thank you Prof. Riley. I have just read your papers, and then found you on KZbin! It is really helpful!

[@elenatello5508]

well explanation, very usefull! thanks!