Assessing complier average causal effects from longitudinal trials with multiple endpoints and treatment noncompliance: An application to a study of Arthritis Health Journal

• Published in: Statistics in medicine Vol. 41; no. 13; pp. 2448 - 2465
• Main Authors: Guo, Lulu, Qian, Yi, Xie, Hui
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 15.06.2022
• Subjects: Arthritis; Arthritis - therapy; causal inference; Causality; Computer Simulation; hierarchical random effects model; Humans; multi‐level model; Patient Compliance; potential outcome model; principal stratification; Randomized Controlled Trials as Topic; treatment effects estimation; Treatment Outcome; principal stratification; treatment effects estimation; causal inference; multi-level model; potential outcome model; hierarchical random effects model
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9364

Treatment noncompliance often occurs in longitudinal randomized controlled trials (RCTs) on human subjects, and can greatly complicate treatment effect assessment. The complier average causal effect (CACE) informs the intervention efficacy for the subpopulation who would comply regardless of assigned treatment and has been considered as patient‐oriented treatment effects of interest in the presence of noncompliance. Real‐world RCTs evaluating multifaceted interventions often employ multiple study endpoints to measure treatment success. In such trials, limited sample sizes, low compliance rates, and small to moderate effect sizes on individual endpoints can significantly reduce the power to detect CACE when these correlated endpoints are analyzed separately. To overcome the challenge, we develop a multivariate longitudinal potential outcome model with stratification on latent compliance types to efficiently assess multivariate CACEs (MCACE) by combining information across multiple endpoints and visits. Evaluation using simulation data shows a significant increase in the estimation efficiency with the MCACE model, including up to 50% reduction in standard errors (SEs) of CACE estimates and 1‐fold increase in the power to detect CACE. Finally, we apply the proposed MCACE model to an RCT on Arthritis Health Journal online tool. Results show that the MCACE analysis detects significant and beneficial intervention effects on two of the six endpoints while estimating CACEs for these endpoints separately fail to detect treatment effect on any endpoint.