{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T17:02:09Z","timestamp":1753894929991,"version":"3.41.2"},"reference-count":18,"publisher":"Becaris Publishing Limited","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Comp. Eff. Res."],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>\n            <jats:bold>Aim:<\/jats:bold>\n            National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an\u00a0STM are optimized to provide closer estimates of the within-trial endpoints.\n            <jats:bold>Materials &amp; methods:<\/jats:bold>\n            An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC\/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan\u2013Meier curves. Sensitivity analyses were conducted to assess uncertainty.\n            <jats:bold>Results:<\/jats:bold>\n            The novel approach resulted in closer estimations to the OS and PFS Kaplan\u2013Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed.\n            <jats:bold>Conclusion:<\/jats:bold>\n            A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.\n          <\/jats:p>","DOI":"10.57264\/cer-2023-0031","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T11:33:56Z","timestamp":1702640036000},"source":"Crossref","is-referenced-by-count":0,"title":["Improved estimation of overall survival and progression-free survival for state transition modeling"],"prefix":"10.57264","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5570-4895","authenticated-orcid":false,"given":"Peter C","family":"Wigfield","sequence":"first","affiliation":[{"name":"Cytel, Weena 316-318, 3012 NJ, Rotterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9226-3232","authenticated-orcid":false,"given":"Bart","family":"Heeg","sequence":"additional","affiliation":[{"name":"Cytel, Weena 316-318, 3012 NJ, Rotterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Ouwens","sequence":"additional","affiliation":[{"name":"AstraZeneca, Pepparedsleden 1, 431 50 M\u00f6lndal, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"36897","reference":[{"key":"e_1_3_8_3_2","unstructured":"Rutherford MJ Lambert PC Sweeting MJ NICE DSU Technical Support Document 21: Flexible Methods for Survival Analysis (2020). https:\/\/nicedsu.sites.sheffield.ac.uk\/tsds\/completed-or-in-progress."},{"issue":"12","key":"e_1_3_8_4_2","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.jval.2020.08.2094","article-title":"Partitioned survival and state transition models for healthcare decision making in oncology: where are we now?","volume":"23","author":"Woods BS","year":"2020","unstructured":"Woods BS, Sideris E, Palmer S, Latimer N, Soares M. 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