Sample size for binary logistic prediction models: Beyond events per variable criteria.

ABSTRACT: Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and

SUBMITTER: van Smeden M 

PROVIDER: S-EPMC6710621 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature