Covariate adjustment and estimation of difference in proportions in randomized clinical trials

TL;DR

This paper improves the estimation of treatment effects in randomized clinical trials with binary outcomes by enhancing covariate adjustment methods, especially under small samples and model misspecification, using a robust variance estimator.

Contribution

It introduces a robust sandwich estimator for the variance of the covariate-adjusted difference in proportions, improving finite sample performance.

Findings

Enhanced accuracy in small samples.

Robustness to model misspecification.

Validated through extensive simulations.

Abstract

Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or G-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model misspecification. We apply the proposed method in a real data example to illustrate the practical utility.