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

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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.

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Acknowledgements

The authors would like to express their gratitude to Professor Allan Wailoo and Professor Paul Tappenden from the University of Sheffield for their valuable feedback on the initial version of the work. The authors would also like to extend their appreciation to the Sheffield Centre for Health and Related Research (SCHARR) library service at the University of Sheffield for their guidance on review methodologies.

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Authors and Affiliations

  1. Sheffield Centre for Health and Related Research (SCHARR), Division of Population Health, School of Medicine and Population Health, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK

    Jen-Yu Amy Chang, James B. Chilcott & Nicholas R. Latimer

  2. Delta Hat Limited, Nottingham, UK

    Nicholas R. Latimer

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  1. Jen-Yu Amy Chang
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  2. James B. Chilcott
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  3. Nicholas R. Latimer
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Correspondence to Jen-Yu Amy Chang.

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Funding

This study was supported by the Wellcome Trust [108903/B/15/Z]. The sponsor had no role in the study design, data collection, analysis, and decision to publish or preparation of the manuscript.

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All authors have no conflicts of interest that are directly relevant to the content of this article.

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Author Contributions

All authors contributed to the conceptualisation and design. J.Y.A.C. performed the literature search and selection, data extraction, evidence synthesis and drafted the original manuscript. J.C. and N.L. contributed insights for quality assessment and interpretation of results, revised the manuscript and provided supervision. All authors reviewed and approved the final manuscript.

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Chang, JY.A., Chilcott, J.B. & Latimer, N.R. Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments. PharmacoEconomics 42, 487–506 (2024). https://doi.org/10.1007/s40273-024-01363-1

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