title: Covariate selection in pharmacometric analyses: a review of methods.
abstract: Covariate selection is an activity routinely performed during pharmacometric analysis. Many are familiar with the stepwise procedures, but perhaps not as many are familiar with some of the issues associated with such methods. Recently, attention has focused on selection procedures that do not suffer from these issues and maintain good predictive properties. In this review, we endeavour to put the main variable selection procedures into a framework that facilitates comparison. We highlight some issues that are unique to pharmacometric analyses and provide some thoughts and strategies for pharmacometricians to consider when planning future analyses.
authors: Ann Arbor Pharmacometrics Group (A2PG), Ann Arbor, MI 48104, USA.
authors: Matthew M
authors: MM
authors: Hutmacher
authors: Kenneth G
authors: KG
authors: Kowalski
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D010599:Pharmacokinetics; D012044:Regression Analysis
keywords: all subset regression; lasso; stepwise procedures; variable selection