Drury, Thomas;
Abellan, Juan J;
Best, Nicky;
White, Ian R;
(2024)
Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation.
Pharmaceutical Statistics
10.1002/pst.2411.
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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 IEs like ‘treatment discontinuation’ using the treatment policy strategy and target the treatment effect on 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 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 with 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.
| Type: | Article |
|---|---|
| Title: | Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/pst.2411 |
| Publisher version: | http://dx.doi.org/10.1002/pst.2411 |
| Language: | English |
| Additional information: | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Pharmaceutical Statistics published by John Wiley & Sons Ltd. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Pharmacology & Pharmacy, Statistics & Probability, Mathematics, estimands, estimation, treatment policy, multiple imputation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10198266 |
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