Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach.

ABSTRACT: Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method account

SUBMITTER: Zhou T 

PROVIDER: S-EPMC8898011 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature