Estimating treatment effect in randomised controlled trials with continuous outcomes subject to non-compliance: a logistic regression-based multiple imputation approach.

ABSTRACT: Non-compliance in randomised controlled trials poses a significant challenge to accurately estimate treatment effects. Traditional methods of analysis, such as intention-to-treat (ITT) and per-protocol (PP) analyses, are known to have limitations. The ITT analysis tends to underestimate the effect of treatment, while PP is prone to selection bias. The Complier Average Causal Effect (CACE) framework has been proposed to address the issue of non-compliance. In this paper, we propose a novel method that assumes the CACE framework based on logistic regression using multiple imputation technique to estimate the unknown compliance status, with a lemma that provides mathematical proof of identification. Its performance is compared with ITT, PP, instrumental variable method and latent class regres

SUBMITTER: Du R 

PROVIDER: S-EPMC12639663 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature