IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v39y2019i8p910-925.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X19881654
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X19881654?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. 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.
    2. 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.
    3. 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.
    4. Jack Cuzick & Peter Sasieni & Jonathan Myles & Jonathan Tyrer, 2007. "Estimating the effect of treatment in a proportional hazards model in the presence of non‐compliance and contamination," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 565-588, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Hiligsmann, Mickaël & Rabenda, Véronique & Bruyère, Olivier & Reginster, Jean-Yves, 2010. "The clinical and economic burden of non-adherence with oral bisphosphonates in osteoporotic patients," Health Policy, Elsevier, vol. 96(2), pages 170-177, July.
    2. Karine Lamiraud & Pierre‐Yves Geoffard, 2007. "Therapeutic non‐adherence: a rational behavior revealing patient preferences?," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1185-1204, November.
    3. Karine Lamiraud & Pierre-Yves Geoffard, 2007. "Therapeutic non-adherence: a rational behavior revealing patient preferences?," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1185-1204.
    4. Katharina E. Blankart & Frank R. Lichtenberg, 2020. "Are patients more adherent to newer drugs?," Health Care Management Science, Springer, vol. 23(4), pages 605-618, December.
    5. Anna M. Wilke & Donald P. Green & Jasper Cooper, 2020. "A placebo design to detect spillovers from an education–entertainment experiment in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1075-1096, June.
    6. Hege Urdahl & Andrea Manca & Mark Sculpher, 2006. "Assessing Generalisability in Model-Based Economic Evaluation Studies," PharmacoEconomics, Springer, vol. 24(12), pages 1181-1197, December.
    7. Stephens Alisa & Joffe Marshall & Keele Luke, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-17, September.
    8. Katherine Baicker & Sendhil Mullainathan & Joshua Schwartzstein, 2015. "Behavioral Hazard in Health Insurance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1623-1667.
    9. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    10. Rotar, Alexandru M. & Preda, Alin & Löblová, Olga & Benkovic, Vanesa & Zawodnik, Szymon & Gulacsi, Laszlo & Niewada, Maciej & Boncz, Imre & Petrova, Guenka & Dimitrova, Maria & Klazinga, Niek, 2018. "Rationalizing the introduction and use of pharmaceutical products: The role of managed entry agreements in Central and Eastern European countries," Health Policy, Elsevier, vol. 122(3), pages 230-236.
    11. Ana Teixeira & Maribel Teixeira & Maria Teresa Herdeiro & Viviana Vasconcelos & Rita Correia & Maria Fernanda Bahia & Isabel F. Almeida & Diogo Guedes Vidal & Hélder Fernando Pedrosa e Sousa & Maria A, 2021. "Knowledge and Practices of Community Pharmacists in Topical Dermatological Treatments," IJERPH, MDPI, vol. 18(6), pages 1-13, March.
    12. Hui Nie & Jing Cheng & Dylan S. Small, 2011. "Inference for the Effect of Treatment on Survival Probability in Randomized Trials with Noncompliance and Administrative Censoring," Biometrics, The International Biometric Society, vol. 67(4), pages 1397-1405, December.
    13. Afschin Gandjour & Karl Wilhelm Lauterbach, 2003. "When Is It Worth Introducing a Quality Improvement Program? A Mathematical Model," Medical Decision Making, , vol. 23(6), pages 518-525, November.
    14. Vincenzo Atella & Federico Belotti & Domenico Depalo, 2017. "Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 106-126, September.
    15. Stephens Alisa & Joffe Marshall & Keele Luke, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1, September.
    16. van Esch, Thamar E.M. & Brabers, Anne E.M. & van Dijk, Christel E. & Gusdorf, Lisette & Groenewegen, Peter P. & de Jong, Judith D., 2017. "Increased cost sharing and changes in noncompliance with specialty referrals in The Netherlands," Health Policy, Elsevier, vol. 121(2), pages 180-188.
    17. Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt, 2023. "Instrumental variable estimation of the causal hazard ratio," Biometrics, The International Biometric Society, vol. 79(2), pages 539-550, June.
    18. Afschin Gandjour & Karl Wilhelm Lauterbach, 2005. "How Much Does It Cost to Change the Behavior of Health Professionals? A Mathematical Model and an Application to Academic Detailing," Medical Decision Making, , vol. 25(3), pages 341-347, May.
    19. Ditte Nørbo Sørensen & Torben Martinussen & Eric Tchetgen Tchetgen, 2019. "A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 639-659, October.
    20. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.

    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:39:y:2019:i:8:p:910-925. 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.