Journal Article

Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials

Chan, Mark Y., Richards, A. Mark, Li, Jialiang, Zhu, Junxian, Tai, Bee-Choo

2025

Overview

Background In randomized clinical trials (RCTs) with non-compliance, evaluating the causal effects of interventions would lead to a more precise estimation of treatment effect when the estimand of interest is the effect of treatment amongst compliers. While there is a large body of literature addressing the issue of non-compliance for continuous, binary, and time-to-event outcomes, this issue is seldom discussed for ordinal outcomes. Methods In this paper, we consider one-sided non-compliance. We introduce an extension of the inverse probability weighting (IPW) method for handling non-compliance involving an ordinal outcome by fully utilizing the information of non-compliance and defining it as a categorical variable to describe the extent of non-compliance. This is in contrast to the usual convention where compliance is regarded as a binary variable. We provide the identification and asymptotic distribution of the proposed method. We compare the proposed method (IPW_Dnew) with intention-to-treat (ITT), per protocol (PP), instrumental variable (IV), and IPW method via a simulation study and real-life data from the JOBS II intervention trial and the IMMACULATE trial. Results Simulation results demonstrate that the proposed method performs better than other methods in terms of bias, coverage, mean squared error, power and Type I error under various scenarios, particularly in situations with selection bias and partial compliance. In the empirical study, a substantial estimate of partial compliance by IPW_Dnew implies that there may be a partial compliance effect. Conclusion For ordinal outcome in the presence of non-compliance, we suggest using the proposed method to estimate the causal effect of treatment amongst compliers and partial compliers, especially when there exists selection bias.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Clinical trials / Compliance / Computer Simulation / Data Interpretation, Statistical / Evaluation / Evidence / Health Sciences / Hospitals / Humans / Inverse probability weighting / Medicine / Medicine & Public Health / Methods / Models, Statistical / Non-compliance / Ordinal outcome / Patient Compliance - statistics & numerical data / Patients / Postpartum depression / Quality management / Randomized clinical trial / Randomized Controlled Trials as Topic - methods / Randomized Controlled Trials as Topic - statistics & numerical data / Right to refuse treatment / Selection bias / Statistical Theory and Methods / Statistics for Life Sciences / Theory of Medicine/Bioethics / Treatment Outcome