Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods.

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Jackson, Christopher Hamilton ; Stevens, John ; Ren, Shijie ; Latimer, Nick ; Bojke, Laura ; Manca, Andrea ; Sharples, Linda (2017)
  • Publisher: Medical decision making : an international journal of the Society for Medical Decision Making
  • Journal: volume 37, issue 4, pages 377-390 (issn: 0272-989X, eissn: 1552-681X)
  • Related identifiers: doi: 10.17863/CAM.18472, doi: 10.1177/0272989X16639900, pmc: PMC5424081
  • Subject: Quality-Adjusted Life Years | Models, Statistical | Data Interpretation, Statistical | United Kingdom | internal medicine | Models, Economic | Uncertainty | multiparameter evidence synthesis | Survival Analysis | technology assessment | Cost-Benefit Analysis | Original Articles | Randomized Controlled Trials as Topic | Methods for Extrapolating Survival in Cost-Effectiveness Analyses | detailed methodology | Humans

This article describes methods used to estimate parameters governing long-term survival, or times to other events, for health economic models. Specifically, the focus is on methods that combine shorter-term individual-level survival data from randomized trials with longer-term external data, thus using the longer-term data to aid extrapolation of the short-term data. This requires assumptions about how trends in survival for each treatment arm will continue after the follow-up period of the trial. Furthermore, using external data requires assumptions about how survival differs between the populations represented by the trial and external data. Study reports from a national health technology assessment program in the United Kingdom were searched, and the findings were combined with “pearl-growing” searches of the academic literature. We categorized the methods that have been used according to the assumptions they made about how the hazards of death vary between the external and internal data and through time, and we discuss the appropriateness of the assumptions in different circumstances. Modeling choices, parameter estimation, and characterization of uncertainty are discussed, and some suggestions for future research priorities in this area are given.
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