Multiple Imputation of Missing Covariates in Randomized Controlled Trials

Abstract

Baseline covariates in randomized experiments play a crucial role in the estimation of treatment effects. Random assignment ensures independence of the covariates and the treatment, which is essential for objective interpretation of the effects. Covariates are also measured before observing the outcome, which guarantees validity of any causal conclusions obtained. When covariates are partly missing, it may be essential to consider if imputations should be carried out respecting randomization or separately by treatment groups. One other question that could arise is if outcome information should be incorporated in the imputations. In view of these considerations, we examine four different ways of multiply imputing missing baseline data in randomized trials. We consider imputation in the design and outcome stages of a randomized experiment, taking into account, the independence restrictions implied by randomization. We allow for the possibility of non-ignorable missingness, and use identifying restrictions in the nonparametric saturated class to obtain the full data density from the observed data distribution. We further conduct repeated sampling studies to assess the performance of the methods in three different missingness scenarios that could commonly emerge in randomized trials.