Sample size considerations for the external validation of a multivariable prognostic model: a resampling study

Collins, Gary S.; Ogundimu, Emmanuel O.; Altman, Douglas G.

doi:10.1002/sim.6787

Sample size considerations for the external validation of a multivariable prognostic model: a resampling study

Collins, Gary S.; Ogundimu, Emmanuel O.; Altman, Douglas G.

Authors

Gary S. Collins

Dr Emmanuel Ogundimu emmanuel.ogundimu@durham.ac.uk
Associate Professor

Douglas G. Altman

Abstract

After developing a prognostic model, it is essential to evaluate the performance of the model in samples independent from those used to develop the model, which is often referred to as external validation. However, despite its importance, very little is known about the sample size requirements for conducting an external validation. Using a large real data set and resampling methods, we investigate the impact of sample size on the performance of six published prognostic models. Focussing on unbiased and precise estimation of performance measures (e.g. the c-index, D statistic and calibration), we provide guidance on sample size for investigators designing an external validation study. Our study suggests that externally validating a prognostic model requires a minimum of 100 events and ideally 200 (or more) events

Citation

Collins, G. S., Ogundimu, E. O., & Altman, D. G. (2016). Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Statistics in Medicine, 35(2), 214-226. https://doi.org/10.1002/sim.6787

Journal Article Type	Article
Acceptance Date	Oct 12, 2015
Online Publication Date	Nov 9, 2015
Publication Date	Jan 30, 2016
Deposit Date	Oct 11, 2020
Publicly Available Date	Oct 15, 2021
Journal	Statistics in Medicine
Print ISSN	0277-6715
Publisher	Wiley
Peer Reviewed	Peer Reviewed
Volume	35
Issue	2
Pages	214-226
DOI	https://doi.org/10.1002/sim.6787

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Copyright Statement
© 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.