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

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.

Standardized mean differences cause funnel plot distortion in publication bias assessments.

Meta-analyses are increasingly used for synthesis of evidence from biomedical research, and often include an assessment of publication bias based on visual or analytical detection of asymmetry in funnel plots. We studied the influence of different normalisation approaches, sample size and intervention effects on funnel plot asymmetry, using empirical datasets and illustrative simulations. We found that funnel plots of the Standardized Mean Difference (SMD) plotted against the standard error (SE) are susceptible to distortion, leading to overestimation of the existence and extent of publication bias.

A generic nomogram for multinomial prediction models: theory and guidance for construction.

Background: The use of multinomial logistic regression models is advocated for modeling the associations of covariates with three or more mutually exclusive outcome categories. As compared to a binary logistic regression analysis, the simultaneous modeling of multiple outcome categories using a multinomial model often better resembles the clinical setting, where a physician typically must distinguish between more than two possible diagnoses or outcome events for an individual patient (e.g.