Woods, B.S., Sideris, E., Palmer, S. et al. (2 more authors) (2020) Partitioned survival and state transition models for healthcare decision making in oncology : where are we now? Value in Health, 23 (12). pp. 1613-1621. ISSN 1098-3015
Abstract
Objectives
Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs).
Methods
In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice.
Results
PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states.
Conclusions
The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 ISPOR-The Professional Society for Health Economics and Outcomes Research. This is an author produced version of a paper subsequently published in Value in Health. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | cost-effectiveness analysis; modeling; partitioned survival; state transition model |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Funding Information: | Funder Grant number Yorkshire Cancer Research S406NL |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Oct 2020 14:22 |
Last Modified: | 02 Feb 2022 17:45 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.jval.2020.08.2094 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167383 |
Downloads
Filename: PSM and STMs 16072020.pdf
Licence: CC-BY-NC-ND 4.0
Filename: Supplementary material 16072020.pdf
Licence: CC-BY-NC-ND 4.0