Sequential linear regression for conditional mean imputation of longitudinal continuous outcomes under reference-based assumptions

TL;DR

This paper introduces a sequential linear regression method for conditional mean imputation in longitudinal clinical trial data, offering a computationally efficient alternative to traditional mixed models under reference-based assumptions.

Contribution

It develops and justifies a new step-wise SLR approach for RBI within the CMI framework, filling a gap in existing methodologies.

Findings

SLR-based RBI performs well in simulations.

The method is computationally more efficient than MMRM.

Application to real data demonstrates practical utility.

Abstract

In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean imputation (CMI) combined with the jackknife estimator of the standard error was proposed as a way to obtain deterministic treatment effect estimates and correct frequentist inference. For both multiple and CMI, a mixed model for repeated measures (MMRM) is often used for the imputation model, but this can be computationally intensive to fit to multiple data sets (e.g. the jackknife samples) and lead to convergence issues with complex MMRM models with many parameters. Therefore, a step-wise approach based on sequential linear regression (SLR) of the outcomes at each visit was developed for the imputation model in the multiple imputation framework, but similar developments in the CMI framework are lacking. In this article, we fill this gap in the literature by proposing a SLR approach to implement RBI in the CMI framework, and justify its validity using theoretical results and simulations. We also illustrate our proposal on a real data application.