Empirical likelihood-based weighted estimation of average treatment effects in randomized clinical trials with missing outcomes

There has been growing attention on covariate adjustment for treatment effect estimation in an objective and efficient manner in randomized clinical trials. In this paper, we propose a weighting approach to extract covariate information based on the empirical likelihood method for the randomized clinical trials with possible missingness in the outcomes. Multiple regression models are imposed to delineate the missing data mechanism and the covariate-outcome relationship, respectively. We demonstrate that the proposed estimator is suitable for objective inference of treatment effects. Theoretically, we prove that the proposed approach is multiply robust and semiparametrically efficient. We conduct simulations and a real data study to make comparisons with other existing methods.