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

• Main Authors: Drury, Thomas, Abellan, Juan J, Best, Nicky, White, Ian R
• Format: Journal Article
• Language: English
• Published: 21-08-2023
• Subjects: Statistics - Applications; Statistics - Methodology
• DOI: 10.48550/arxiv.2308.10857

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.