Robust extraction of covariate information to improve estimation efficiency in randomized trials.

Abstract

In randomized trials, investigators typically rely upon an unadjusted estimate of the mean outcome within each treatment arm to draw causal inferences. Statisticians have underscored the gain in efficiency that can be achieved from covariate adjustment in randomized trials with a focus on problems involving linear models. Despite recent theoretical advances, there has been a reluctance to adjust for covariates based on two primary reasons: (i) covariate-adjusted estimates based on conditional logistic regression models have been shown to be less precise and (ii) concern over the opportunity to manipulate the model selection process for covariate adjustments to obtain favorable results. In this paper, we address these two issues and summarize recent theoretical results on which is based a proposed general methodology for covariate adjustment under the framework of targeted maximum likelihood estimation in trials with two arms where the probability of treatment is 50%. The proposed methodology provides an estimate of the true causal parameter of interest representing the population-level treatment effect. It is compared with the estimates based on conditional logistic modeling, which only provide estimates of subgroup-level treatment effects rather than marginal (unconditional) treatment effects. We provide a clear criterion for determining whether a gain in efficiency can be achieved with covariate adjustment over the unadjusted method. We illustrate our strategy using a resampled clinical trial dataset from a placebo controlled phase 4 study. Results demonstrate that gains in efficiency can be achieved even with binary outcomes through covariate adjustment leading to increased statistical power.