Calculating the sample size required for developing a clinical prediction model

By R. Riley, J. Ensor, Kym I. E. Snell, Frank E. Harrell, G. Martin, Johannes B Reitsma, Karel G. M. Moons, Gary S. Collins, M. van Smeden. British medical journal, 2020

2,194 citations recorded by the source corpus.

Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.

Subfields: Epidemiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dc0c0bc47dbb8a5110710288447c16f0cdcfc1fc

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In Epidemiology

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Citations in the corpus

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, methodology): “For regression-based methods 48 , and a selection of machine-learning-based methods 47 , technique-speci ﬁ c a priori sample size calculations are available, although for many model architectures and settings (e.g., semi-supervised learning, decision trees, or convolutional neural networks) no…”
Developing clinical prediction models: a step-by-step guide (2024, positive): “Calculating sample size requirements for a specific model Riley and colleagues 50 provide helpful guidance and code 51 52 on sample size calculations.”