Résumé
A growing number of covariate modeling methods have been proposed in the field of popPK modeling, but limited information exists on how they all compare. The objective of this study was to perform a systematic review of all popPK covariate modeling methods, focusing on assessing the existing knowledge on their performances. For each method of each article included in this review, evaluation setting, performance metrics along with their associated values, and relative computational times were reported when available. Evaluation settings report was done for uncertainty assessment of communicated results. Results showed that EBEs‐based ML methods stood out as the best covariate selection methods. AALASSO, a hybrid genetic algorithm, FREM with a clinical significance criterion and SCM+ with stagewise filtering were the best covariate model selection techniques—AALASSO being the very best one. Results also showed a lack of consensus on how to benchmark simulated datasets of different scenarios when evaluating method performances, but also on which metrics to use for method evaluation. We propose to systematically report TPR (sensitivity), FPR (Type I error), FNR (Type II error), TNR (specificity), covariate parameter error bias (MPE) and precision (RMSE), clinical relevance, and model fitness by means of BIC, concentration prediction error bias (MPE), and precision (RMSE) of new proposed methods and compare them with SCM. We propose to systematically combine covariate selection techniques to SCM or FFEM to allow for comparison with SCM. We also highlight the need for an open‐source benchmark of simulated datasets on a representative set of scenarios.