Estimation of treatment policy estimands for continuous outcomes using off treatment sequential multiple imputation

TL;DR

This paper proposes and evaluates multiple imputation models to accurately estimate treatment effects on continuous outcomes in clinical trials, accounting for treatment discontinuation and reducing bias compared to traditional methods.

Contribution

It introduces new MI models that handle differences in outcomes before and after treatment discontinuation, improving bias correction in treatment effect estimation.

Findings

Ignoring discontinuation leads to bias and underestimated variability.

Some MI models effectively correct bias but increase variance.

Model choice depends on trial design and data characteristics.

Abstract

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline "intercurrent" events (IEs) are to be handled. In late-stage clinical trials, it is common to handle intercurrent events like "treatment discontinuation" using the treatment policy strategy and target the treatment effect on all outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both of these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared to patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.