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Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

Author

Listed:
  • Jen-Yu Amy Chang

    (University of Sheffield)

  • James B. Chilcott

    (University of Sheffield)

  • Nicholas R. Latimer

    (University of Sheffield
    Delta Hat Limited)

Abstract

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a ‘roadmap’ of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines—statistics, epidemiology, causal inference, operational research and computer science—has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Suggested Citation

  • Jen-Yu Amy Chang & James B. Chilcott & Nicholas R. Latimer, 2024. "Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments," PharmacoEconomics, Springer, vol. 42(5), pages 487-506, May.
  • Handle: RePEc:spr:pharme:v:42:y:2024:i:5:d:10.1007_s40273-024-01363-1
    DOI: 10.1007/s40273-024-01363-1
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    References listed on IDEAS

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    1. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    2. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884, Decembrie.
    3. 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.
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