Statistics

Chromatography

Cross-validation

Resampling

Data mining

Calibration

Computer science

Statistic

Mathematics

Data set

Data science

Artificial intelligence

Sample size determination

Chemistry

Sample (material)

Model validation

Statistics in Medicine

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.

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