Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes

Abstract

Missing data are unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.

Keywords:

• Longitudinal clinical trial
• missing data
• multiple imputation
• robust regression
• sensitivity analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Yang is partially supported by the NSF (National Institutes of Health) [Grant Number DMS-1811245], NIA (National Science Foundation) [Grant Number 1R01AG066883], and NIEHS [Grant Number 1R01ES031651].