Variable selection for covariate-adjusted semiparametric inference in randomized clinical trials.

ABSTRACT: Extensive baseline covariate information is routinely collected on participants in randomized clinical trials, and it is well recognized that a proper covariate-adjusted analysis can improve the efficiency of inference on the treatment effect. However, such covariate adjustment has engendered considerable controversy, as post hoc selection of covariates may involve subjectivity and may lead to biased inference, whereas prior specification of the adjustment may exclude important variables from consideration. Accordingly, how to select covariates objectively to gain maximal efficiency is of broad interest. We propose and study the use of modern variable selection methods for this purpose in the context of a semiparametric framework, under which variable selection in modeling the relationship

SUBMITTER: Yuan S 

PROVIDER: S-EPMC3855673 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature