When patients’ voices aren’t heard: estimands and statistical methods for handling missing patient-reported outcomes in oncology studies

When patients’ voices aren’t heard: estimands and statistical methods for handling missing patient-reported outcomes in oncology studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When patients’ voices aren’t heard: estimands and statistical methods for handling missing patient-reported outcomes in oncology studies Emma Martin, Rachael Lawrance, Alex Hind, Suzie Cro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8533090/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Medical Research Methodology → Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: Patient-reported outcomes (PROs) are integral to oncology clinical trials, yet missing data - especially due to intercurrent events (ICEs) including disease progression and death - poses 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 in addition to handling treatment discontinuation and death, 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. patient-reported outcome (PRO) estimand oncology clinical trial missing data sensitivity analysis Full Text Additional Declarations Competing interest reported. EM, RL and AH are employed by Adelphi Values Ltd. SC has no competing interests to declare. Supplementary Files Supplementarymaterialv20.docx Cite Share Download PDF Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 14 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 12 Jan, 2026 Editor invited by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8533090","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597594000,"identity":"390f5a33-b93f-4ac1-aa98-3a9fd610f359","order_by":0,"name":"Emma Martin","email":"","orcid":"","institution":"Adelphi Values Ltd","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Martin","suffix":""},{"id":597594001,"identity":"cdd6fa5d-e0a1-4c70-977f-44f812433884","order_by":1,"name":"Rachael Lawrance","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACNiC2YOCDcfmJ1CIBJsFAsoFkLQYHCKg3bz/87MGPCqAW9jNmH37usJMzPt6d+IGhxiYalxaZM2nmhj1ngFp4coxn9p5JNjY7c3azBMOxtFxcDpRgyGGT4G0DOSx3MwNv24HEbTdytzEwNhzGrYX/DZvk339ALfxvNzP+bTtQv3kGIS0SOWzSvA1ALRK5m5mBtiQYSBDU8sxMWuaYBA+bxPvPzLJtyYYzQH5JwOcX/uRnkm9qbOT4+dOSGd+22cnzt/du/PChxganFhjgQeUmEFA+CkbBKBgFowA/AADk8U4+hYEqggAAAABJRU5ErkJggg==","orcid":"","institution":"Adelphi Values Ltd","correspondingAuthor":true,"prefix":"","firstName":"Rachael","middleName":"","lastName":"Lawrance","suffix":""},{"id":597594002,"identity":"6d0b3f52-4858-4b4b-9c78-7aae19d6b151","order_by":2,"name":"Alex Hind","email":"","orcid":"","institution":"Adelphi Values Ltd","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Hind","suffix":""},{"id":597594003,"identity":"1eba1a5e-83be-43e4-bb8f-575582b645af","order_by":3,"name":"Suzie Cro","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Suzie","middleName":"","lastName":"Cro","suffix":""}],"badges":[],"createdAt":"2026-01-06 15:39:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8533090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8533090/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12874-026-02862-9","type":"published","date":"2026-05-01T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":108437703,"identity":"0ba406d7-a4b1-4b79-a36b-68c900a937a0","added_by":"auto","created_at":"2026-05-04 16:02:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":558632,"visible":true,"origin":"","legend":"","description":"","filename":"MissingpatientreportedoutcomesinoncologymanuscriptBMCv20updated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8533090/v1_covered_9f0ab17a-0fc7-448c-b914-8a2acfba31c4.pdf"},{"id":103608184,"identity":"e392b8ab-5cb8-40cf-a5e2-a8e112190123","added_by":"auto","created_at":"2026-02-27 15:18:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39298,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialv20.docx","url":"https://assets-eu.researchsquare.com/files/rs-8533090/v1/fd7edc3a03fe96cca6e3f780.docx"}],"financialInterests":"Competing interest reported. 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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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe 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 in addition to handling treatment discontinuation and death, 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).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe 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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur recommendations provide practical guidance for specifying estimands, selecting statistical analysis methods, and interpreting PRO analyses in oncology trials with missing data. 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