pmsampsize

pmsampsize computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. 2018. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model. For continuous outcomes, there are four criteria: i) small overfitting defined by an expected shrinkage of predictor effects by 10% or less, ii) small absolute difference of 0.05 in the model's apparent and adjusted R-squared value, iii) precise estimation of the residual standard deviation, and iv) precise estimation of the average outcome value. The sample size calculation requires the user to pre-specify (e.g. based on previous evidence) the anticipated R-squared of the model, and the average outcome value and standard deviation of outcome values in the population of interest. For binary or survival (time-to-event) outcomes, there are three criteria: i) small overfitting defined by an expected shrinkage of predictor effects by 10% or less, ii) small absolute difference of 0.05 in the model's apparent and adjusted Nagelkerke's R-squared value, and iii) precise estimation (within +/- 0.05) of the average outcome risk in the population for a key timepoint of interest for prediction.