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

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  • Christopher Jackson
  • John Stevens
  • Shijie Ren
  • Nick Latimer
  • Laura Bojke
  • Andrea Manca
  • Linda Sharples

Abstract

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.

Suggested Citation

  • Christopher Jackson & John Stevens & Shijie Ren & Nick Latimer & Laura Bojke & Andrea Manca & Linda Sharples, 2017. "Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods," Medical Decision Making, , vol. 37(4), pages 377-390, May.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:4:p:377-390
    DOI: 10.1177/0272989X16639900
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    References listed on IDEAS

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    1. Simon Walker & Mark Sculpher & Karl Claxton & Steve Palmer, 2012. "Coverage with evidence development, only in research, risk sharing or patient access scheme? A framework for coverage decisions," Working Papers 077cherp, Centre for Health Economics, University of York.
    2. David H. Howard & Florence K. Tangka & Laura C. Seeff & Lisa C. Richardson & Donatus U. Ekwueme, 2009. "The impact of detection and treatment on lifetime medical costs for patients with precancerous polyps and colorectal cancer," Health Economics, John Wiley & Sons, Ltd., vol. 18(12), pages 1381-1393, December.
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    2. Mathyn Vervaart & Eline Aas & Karl P. Claxton & Mark Strong & Nicky J. Welton & Torbjørn Wisløff & Anna Heath, 2023. "General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations," Medical Decision Making, , vol. 43(5), pages 595-609, July.
    3. Adrian Vickers, 2019. "An Evaluation of Survival Curve Extrapolation Techniques Using Long-Term Observational Cancer Data," Medical Decision Making, , vol. 39(8), pages 926-938, November.
    4. Fuhmei Wang & Jung-Der Wang & Yu-Wen Hung, 2018. "Universal health insurance, health inequality and oral cancer in Taiwan," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-13, October.
    5. Zhaojing Che & Nathan Green & Gianluca Baio, 2023. "Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment," Medical Decision Making, , vol. 43(3), pages 299-310, April.
    6. Jing-Shiang Hwang & Tsuey-Hwa Hu, 2020. "Later-Life Exposure to Moderate PM 2.5 Air Pollution and Life Loss of Older Adults in Taiwan," IJERPH, MDPI, vol. 17(6), pages 1-12, March.

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