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Statistical Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Systematic Review of Methodological Papers

Author

Listed:
  • Abualbishr Alshreef

    (Health Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire, UK)

  • Nicholas Latimer

    (Health Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire, UK)

  • Paul Tappenden

    (Health Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire, UK)

  • Ruth Wong

    (Health Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire, UK)

  • Dyfrig Hughes

    (Centre for Health Economics & Medicines Evaluation (CHEME), Bangor University, Bangor, Gwynedd, UK)

  • James Fotheringham

    (Sheffield Kidney Institute, Sheffield Teaching Hospitals NHS Trust, Sheffield, South Yorkshire, UK)

  • Simon Dixon

    (Health Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire, UK)

Abstract

Introduction. Medication nonadherence can have a significant negative impact on treatment effectiveness. Standard intention-to-treat analyses conducted alongside clinical trials do not make adjustments for nonadherence. Several methods have been developed that attempt to estimate what treatment effectiveness would have been in the absence of nonadherence. However, health technology assessment (HTA) needs to consider effectiveness under real-world conditions, where nonadherence levels typically differ from those observed in trials. With this analytical requirement in mind, we conducted a review to identify methods for adjusting estimates of treatment effectiveness in the presence of patient nonadherence to assess their suitability for use in HTA. Methods. A “Comprehensive Pearl Growing†technique, with citation searching and reference checking, was applied across 7 electronic databases to identify methodological papers for adjusting time-to-event outcomes for nonadherence using individual patient data. A narrative synthesis of identified methods was conducted. Methods were assessed in terms of their ability to reestimate effectiveness based on alternative, suboptimal adherence levels. Results. Twenty relevant methodological papers covering 12 methods and 8 extensions to those methods were identified. Methods are broadly classified into 4 groups: 1) simple methods, 2) principal stratification methods, 3) generalized methods (g-methods), and 4) pharmacometrics-based methods using pharmacokinetics and pharmacodynamics (PKPD) analysis. Each method makes specific assumptions and has associated limitations. Five of the 12 methods are capable of adjusting for real-world nonadherence, with only g-methods and PKPD considered appropriate for HTA. Conclusion. A range of statistical methods is available for adjusting estimates of treatment effectiveness for nonadherence, but most are not suitable for use in HTA. G-methods and PKPD appear to be more appropriate to estimate effectiveness in the presence of real-world adherence.

Suggested Citation

  • Abualbishr Alshreef & Nicholas Latimer & Paul Tappenden & Ruth Wong & Dyfrig Hughes & James Fotheringham & Simon Dixon, 2019. "Statistical Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Systematic Review of Method," Medical Decision Making, , vol. 39(8), pages 910-925, November.
    • Handle: RePEc:sae:medema:v:39:y:2019:i:8:p:910-925
    DOI: 10.1177/0272989X19881654
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    References listed on IDEAS

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    1. Cleemput, Irina & Kesteloot, Katrien & DeGeest, Sabina, 2002. "A review of the literature on the economics of noncompliance. Room for methodological improvement," Health Policy, Elsevier, vol. 59(1), pages 65-94, January.
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    3. Pasi Korhonen & Juni Palmgren, 2002. "Effect modification in a randomized trial under non‐ignorable non‐compliance: an application to the alpha‐tocopherol beta‐carotene study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(1), pages 115-133, January.
    4. Dyfrig A. Hughes & Adrian Bagust & Alan Haycox & Tom Walley, 2001. "The impact of non‐compliance on the cost‐effectiveness of pharmaceuticals: a review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 10(7), pages 601-615, October.
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    Cited by:

    1. Donald A. Redelmeier & Deva Thiruchelvam & Robert J. Tibshirani, 2022. "Testing for a Sweet Spot in Randomized Trials," Medical Decision Making, , vol. 42(2), pages 208-216, February.

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