The randomized clinical trial (RCT) is the preferred study design for assessing causal effects of medical interventions. A patient and their treatment decision makers are often interested in intervention efficacy that informs what to expect when the patient actually complies with treatment.
In many real-world RCTs, however, the patient-oriented intervention effect is often challenging to evaluate because of limited sample size, a small number of compliers due to low compliance rate and small to moderate effect size on outcome measures, which can significantly reduce the power to detect intervention efficacy.
Furthermore, in many RCTs, especially when evaluating multifaceted interventions for chronic diseases, such as arthritis, the endpoints often involve multiple outcomes to measure a complex trait. This raises the challenge of how to optimally pool treatment efficacy estimation across outcome measures. The “complier-average causal effect” (CACE) approaches have become popular in informing such patient-oriented treatment effects.
Our study has developed a novel CACE approach, called the MCACE model, to analyze the complicated data from real-world RCTs.
Comparing the new approach to existing approaches, such as the intention-to-treat and univariate CACE analysis, our new methods have shown improved efficiency and robustness—specifically, for estimating intervention efficacy, and on multiple endpoints in real-world clinical trials.
ENAR 2021 Spring Meeting
2021 Joint Statistical Meetings
2018 and 2019 Annual workshop on research methods for patients and researchers at Arthritis Research Canada, by trainees
2021 Monthly Research Webinar in Arthritis Research Canada, by trainee
Guo, L., Qian, Y., and Xie, H (2022) Assessing Complier Average Causal Effects from Longitudinal Trials with Multiple Endpoints and Treatment Noncompliance: an Application to a Study of Arthritis Health Journal. Statistics in Medicine. Forthcoming.