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Sample size considerations for the external validation of a multivariable prognostic model: a resampling study
Author(s) -
Collins Gary S.,
Ogundimu Emmanuel O.,
Altman Douglas G.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6787
Subject(s) - resampling , sample size determination , statistic , computer science , statistics , sample (material) , calibration , cross validation , data mining , mathematics , chemistry , chromatography
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. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.