When patients’ voices aren’t heard: estimands and statistical methods for handling missing patient-reported outcomes in oncology studies
File(s)
Published version
Author(s)
Martin, Emma
Lawrance, Rachael
Hind, Alex
Cro, Suzie
Type
Journal Article
Abstract
Introduction: Patient-reported outcomes (PROs) are integral to oncology clinical trials, yet missing data - especially due to intercurrent events (ICEs) of disease progression pose challenges for robust and interpretable analysis. While regulatory and best practice guidelines now emphasize the explicit definition of estimands, including strategies for handling ICEs, and supplementary analyses to examine their robustness, practical recommendations for their implementation in PRO analyses remain limited.
Methods: We present a methodological framework for defining estimands and statistical analysis for a longitudinal change in a PRO confirmatory endpoint, including strategies for handling the main ICE of disease progression using a simulated clinical trial. We propose for the ICE of disease progression using either a hypothetical or treatment policy strategy for the main and supplementary analysis, and present implementation of two methods targeting a hypothetical approach and one method for treatment policy approach (implicit multiple imputation in a longitudinal model, a joint modelling of longitudinal PROs and time-to-progression, and multiple imputation using control-based imputation post progression).
Results: We present the occurrence of ICEs and missing data and provide a tutorial for conducting analysis in the presence of disease progression using hypothetical and treatment policy strategies respectively. Despite the occurrence of disease progression events and other missing data, conducting supplementary analysis provided confidence in our overall interpretation for the simulated trial.
Conclusions: Our recommendations provide practical guidance for specifying estimands, selecting statistical analysis methods, and interpreting PRO analyses in oncology trials with missing data. Accurately estimating the treatment effect on quality-of-life, in a way which is interpretable, is crucial to aid patients and other stakeholders when making treatment decisions.
Methods: We present a methodological framework for defining estimands and statistical analysis for a longitudinal change in a PRO confirmatory endpoint, including strategies for handling the main ICE of disease progression using a simulated clinical trial. We propose for the ICE of disease progression using either a hypothetical or treatment policy strategy for the main and supplementary analysis, and present implementation of two methods targeting a hypothetical approach and one method for treatment policy approach (implicit multiple imputation in a longitudinal model, a joint modelling of longitudinal PROs and time-to-progression, and multiple imputation using control-based imputation post progression).
Results: We present the occurrence of ICEs and missing data and provide a tutorial for conducting analysis in the presence of disease progression using hypothetical and treatment policy strategies respectively. Despite the occurrence of disease progression events and other missing data, conducting supplementary analysis provided confidence in our overall interpretation for the simulated trial.
Conclusions: Our recommendations provide practical guidance for specifying estimands, selecting statistical analysis methods, and interpreting PRO analyses in oncology trials with missing data. Accurately estimating the treatment effect on quality-of-life, in a way which is interpretable, is crucial to aid patients and other stakeholders when making treatment decisions.
Date Issued
2026-06-19
Date Acceptance
2026-04-21
Citation
BMC Medical Research Methodology, 2026, 26
ISSN
1471-2288
Publisher
BMC
Journal / Book Title
BMC Medical Research Methodology
Volume
26
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Publication Status
Published
Article Number
141
Date Publish Online
2026-05-01