Covariate Adjustment in Randomized Clinical Trials with Missing Covariate and Outcome Data

TL;DR

This paper reviews and proposes covariate adjustment methods for randomized clinical trials with missing data, demonstrating improved precision in treatment effect estimates through simulations and real data application.

Contribution

It introduces a full weighting approach combining inverse probability and overlap weighting for handling missing covariate and outcome data in clinical trials.

Findings

Adjustment methods improve treatment effect estimate precision.

Including interaction terms enhances model performance.

Proposed methods outperform common alternatives in simulations.

Abstract

When analyzing data from randomized clinical trials, covariate adjustment can be used to account for chance imbalance in baseline covariates and to increase precision of the treatment effect estimate. A practical barrier to covariate adjustment is the presence of missing data. In this paper, in the light of recent theoretical advancement, we first review several covariate adjustment methods with incomplete covariate data. We investigate the implications of the missing data mechanism on estimating the average treatment effect in randomized clinical trials with continuous or binary outcomes. In parallel, we consider settings where the outcome data are fully observed or are missing at random; in the latter setting, we propose a full weighting approach that combines inverse probability weighting for adjusting missing outcomes and overlap weighting for covariate adjustment. We highlight the importance of including the interaction terms between the missingness indicators and covariates as predictors in the models. We conduct comprehensive simulation studies to examine the finite-sample performance of the proposed methods and compare with a range of common alternatives. We find that conducting the proposed adjustment methods generally improves the precision of treatment effect estimates regardless of the imputation methods when the adjusted covariate is associated with the outcome. We apply the methods to the Childhood Adenotonsillectomy Trial to assess the effect of adenotonsillectomy on neurocognitive functioning scores.