Inverse probability weighted estimation of dynamic treatment regimen means in sequential multiple assignment randomised trials with missing data: a simulation study

Inverse probability weighted estimation of dynamic treatment regimen means in sequential multiple assignment randomised trials with missing data: a simulation study | 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 Inverse probability weighted estimation of dynamic treatment regimen means in sequential multiple assignment randomised trials with missing data: a simulation study Jessica Xu, Robert K Mahar, Katherine J Lee, Anurika P De Silva, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7419071/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Trials → Version 1 posted 5 You are reading this latest preprint version Abstract Background Dynamic treatment regimens (DTRs) guide personalised sequential treatment decisions for patients with chronic or progressive conditions. Sequential multiple assignment randomised trials (SMARTs) are designed to evaluate and optimise DTRs by randomising participants at multiple stages based on intermediate outcomes. To identify optimal DTRs in SMARTs, the mean outcome of each DTR is often estimated via inverse probability weighting (IPW), a statistical method that uses the inverse probability of treatment to address potential bias in the design. Like other randomised controlled trials, SMARTs are subject to missing data. Handling missing data in SMARTs is complicated by the sequential randomisation and dependence on intermediate outcomes. We evaluated the performance of complete case analysis (CCA) and multiple imputation (MI) for handling missing data when estimating the DTR mean outcomes using IPW in a two-stage SMART. Methods We simulated 1000 datasets of 400 participants, based on a prototypical SMART design with two stages where only non-responders are re-randomised at stage 2. The estimands of interest were the four DTR means of a continuous outcome and were estimated using IPW. We defined four plausible missing data scenarios using missing data directed acyclic graphs (m-DAGs) and then assessed how each missing data method (CCA and MI) performed under different proportions of missingness (20%, 40%) and strengths of associations with missingness in stage 1 intermediate outcome, stage 2 treatment and the final outcome. Results Minimal bias was observed with MI when estimating the mean outcomes of the DTRs in most scenarios, except for when stage 1 intermediate outcome was missing dependent on baseline variables and stage 1 treatment. When data were missing dependent on other variables (for example, stage 2 treatment missing dependent on stage 1 intermediate outcome), CCA generally showed greater bias than MI when estimating the mean outcomes of the DTRs. Empirical standard errors were comparable across both missing data methods, with MI generally producing slightly lower values. Conclusion We found that for a prototypical SMART design, MI generally showed close to zero bias and slightly lower standard errors compared to CCA when IPW was used to estimate the mean outcomes of DTRs. Sequential multiple assignment randomised trials Dynamic treatment regimens Missing data Inverse probability weighting Multiple imputation Full Text Supplementary Files Additionalfile1final.docx Additionalfile2final.docx Additionalfile3final.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Trials → Version 1 posted Editorial decision: Major revision 16 Nov, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers invited by journal 09 Sep, 2025 Editor assigned by journal 29 Aug, 2025 First submitted to journal 27 Aug, 2025 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-7419071","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512548521,"identity":"33d17cc3-d1aa-4920-bb03-6ca8f0b33b3c","order_by":0,"name":"Jessica 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