IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v43y2023i5p610-620.html
   My bibliography  Save this article

A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment

Author

Listed:
  • Ash Bullement

    (School of Health and Related Research, University of Sheffield, UK
    Delta Hat Limited, Nottingham, UK)

  • Matthew D. Stevenson

    (School of Health and Related Research, University of Sheffield, UK)

  • Gianluca Baio

    (Department of Statistical Science, University College London, UK)

  • Gemma E. Shields

    (School of Health Sciences, University of Manchester, UK)

  • Nicholas R. Latimer

    (School of Health and Related Research, University of Sheffield, UK)

Abstract

Background External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare. Purpose This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA. Data Sources Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking. Study Selection Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation. Data Extraction Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods. Data Synthesis Across 18 methods identified from 22 studies, themes included use of informative prior(s) ( n  = 5), piecewise ( n  = 7), and general population adjustment ( n  = 9), plus a variety of “other†( n  = 8) approaches. Most methods were applied in cancer populations ( n  = 13). No studies compared or validated their method against another method that also incorporated external evidence. Limitations As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review. Conclusions Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research. Highlights This review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment. We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of “other†approaches. No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

Suggested Citation

  • Ash Bullement & Matthew D. Stevenson & Gianluca Baio & Gemma E. Shields & Nicholas R. Latimer, 2023. "A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment," Medical Decision Making, , vol. 43(5), pages 610-620, July.
  • Handle: RePEc:sae:medema:v:43:y:2023:i:5:p:610-620
    DOI: 10.1177/0272989X231168618
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X231168618
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X231168618?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Helen Bell Gorrod & Ben Kearns & John Stevens & Praveen Thokala & Alexander Labeit & Nicholas Latimer & David Tyas & Ahmed Sowdani, 2019. "A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement," Medical Decision Making, , vol. 39(8), pages 899-909, November.
    2. James Larkin & Anthony J Hatswell & Paul Nathan & Maximilian Lebmeier & Dawn Lee, 2015. "The Predicted Impact of Ipilimumab Usage on Survival in Previously Treated Advanced or Metastatic Melanoma in the UK," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    3. Patricia Guyot & Anthony E. Ades & Matthew Beasley & Béranger Lueza & Jean-Pierre Pignon & Nicky J. Welton, 2017. "Extrapolation of Survival Curves from Cancer Trials Using External Information," Medical Decision Making, , vol. 37(4), pages 353-366, May.
    4. Jing‐Shiang Hwang & Tsuey‐Hwa Hu & Lukas Jyuhn‐Hsiarn Lee & Jung‐Der Wang, 2017. "Estimating lifetime medical costs from censored claims data," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 332-344, December.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Daniel Gallacher & Peter Kimani & Nigel Stallard, 2022. "Biased Survival Predictions When Appraising Health Technologies in Heterogeneous Populations," PharmacoEconomics, Springer, vol. 40(1), pages 109-120, January.
    3. Taihang Shao & Mingye Zhao & Leyi Liang & Lizheng Shi & Wenxi Tang, 2023. "Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067," PharmacoEconomics - Open, Springer, vol. 7(3), pages 383-392, May.
    4. 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.
    5. M. Campioni & I. Agirrezabal & R. Hajek & J. Minarik & L. Pour & I. Spicka & S. Gonzalez-McQuire & P. Jandova & V. Maisnar, 2020. "Methodology and results of real-world cost-effectiveness of carfilzomib in combination with lenalidomide and dexamethasone in relapsed multiple myeloma using registry data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(2), pages 219-233, March.
    6. Marjolein J. E. Greuter & Xiang‐Ming Xu & Jie‐Bin Lew & Evelien Dekker & Ernst J. Kuipers & Karen Canfell & Gerrit A. Meijer & Veerle M. H. Coupé, 2014. "Modeling the Adenoma and Serrated Pathway to Colorectal CAncer (ASCCA)," Risk Analysis, John Wiley & Sons, vol. 34(5), pages 889-910, May.
    7. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    8. Daniel Gallacher & Peter Kimani & Nigel Stallard, 2021. "Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study," Medical Decision Making, , vol. 41(1), pages 37-50, January.
    9. Szu-Chun Yang & Wu-Wei Lai & Jason C Hsu & Wu-Chou Su & Jung-Der Wang, 2020. "Comparative effectiveness and cost-effectiveness of three first-line EGFR-tyrosine kinase inhibitors: Analysis of real-world data in a tertiary hospital in Taiwan," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-13, April.
    10. 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.
    11. Jonathan Dando & Maximilian Lebmeier, 2020. "A novel valuation model for medical intervention development based on progressive dynamic changes that integrates Health Technology Assessment outcomes with early-stage innovation and indication-speci," Journal of Innovation and Entrepreneurship, Springer, vol. 9(1), pages 1-28, December.
    12. Yang Meng & Nadine Hertel & John Ellis & Edith Morais & Helen Johnson & Zoe Philips & Neil Roskell & Andrew Walker & Dawn Lee, 2018. "The cost-effectiveness of nivolumab monotherapy for the treatment of advanced melanoma patients in England," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1163-1172, November.
    13. Adam Dvir, 2022. "Is mass media an effective channel for conveying nutritional information? Welfare implications of the WHO classification of processed meats as carcinogenic on consumers in Israel," French Stata Users' Group Meetings 2022 21, Stata Users Group.
    14. Philip Cooney & Arthur White, 2023. "Direct Incorporation of Expert Opinion into Parametric Survival Models to Inform Survival Extrapolation," Medical Decision Making, , vol. 43(3), pages 325-336, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:43:y:2023:i:5:p:610-620. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.