Sample size calculations for external validation of a clinical prediction model

In prediction model research, external validation is needed to examine an existing model’s performance using data independent to that used for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise model performance estimates.
In this talk, I propose how to determine the sample size needed for a new external validation study, where the aim is to precisely estimate a model's performance in terms of calibration, discrimination and clinical utility. The work applies to any model, for example developed using statistical or machine learning. Read the full papers here:

[fabianb]

Very interesting! Is there code in R or Python available for this?