Robust Two-Stage Adjustment for Non-Adherence in Cluster-Randomized Trials

Thomas M. Menino Convention & Exhibition Center 

In a randomized trial with perfect compliance, unadjusted estimators of treatment efficacy are unbiased. However, when trial participants do not receive the treatment to which they were randomized (referred to as non-adherence), unadjusted estimators may incur substantial bias. Correction of this bias is made more complicated in a cluster-randomized trial (CRT) than in individually-randomized settings due to the hierarchical nature of the data. Building upon prior work for missing outcome data in CRTs, we propose a two-stage estimation procedure utilizing targeted maximum likelihood to obtain robust and asymptotically semi-parametric efficient estimators of treatment efficacy in the presence of non-adherence. We outline theoretical arguments under which our proposed procedure accounts for non-adherence at the cluster level, at the individual level, and at both levels simultaneously. In simulation experiments, we verify that the procedure corrects bias and enhances precision compared to unadjusted analyses. Our work provides a foundation for flexible and robust non-adherence adjustment in parallel-arm CRTs, leveraging causal machine learning to mitigate bias and improve efficiency.

cluster randomized trial

non-adherence

causal inference

targeted maximum likelihood estimation

machine learning

two stage estimation 

Section on Statistics in Epidemiology