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

TL;DR

This paper introduces a robust, non-parametric framework for analyzing longitudinal clinical trial data with missing outcomes and non-normal distributions, improving bias reduction and power in treatment effect estimation.

Contribution

It develops a general robust approach using sequential weighted regressions under control-based imputation without parametric assumptions, ensuring consistency and robustness.

Findings

Method outperforms traditional models in simulations

Achieves unbiased ATE estimates with heavy-tailed data

Demonstrated effectiveness on AIDS trial data

Abstract

Missing data is 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 general 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 both 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.