Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster randomized trials with missing outcomes

TL;DR

This paper introduces a doubly robust augmented generalized estimating equations (AUG) method with inverse probability weighting for estimating treatment effects in cluster-randomized trials with missing outcomes, accounting for interactions and covariate interference.

Contribution

It develops a novel AUG-IPW estimator that handles interactions, missing data, and covariate interference, providing consistent estimates under model misspecification.

Findings

The proposed method is doubly robust to model misspecification.

Simulation studies show improved bias and efficiency over existing methods.

Application to HIV trial data demonstrates practical utility.

Abstract

Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR), it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method.