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

By Gary S. Collins, E. Ogundimu, Douglas G. Altman. Statistics in Medicine, 2015

591 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5e804f9870838b71b99f3093ec2befb703242c98

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Citations in the corpus

External validation of prognostic models: what, why, how, when and where? (2020, background): “Some simulation studies have shown that in validation a minimum of 100 events (and 100 non-events) are needed and 200 events are preferred [33, 34].”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “Further, sample size was often small, with 46% having fewer than 100 events, which is a minimum effective sample size suggested for external validation 40 41 (although an increase to 200 was recently proposed to assess calibration 37 41 ).”