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Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations

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  • Andrea Gabrio

    (University College London)

  • Alexina J. Mason

    (London School of Hygiene and Tropical Medicine)

  • Gianluca Baio

    (University College London)

Abstract

Cost-effectiveness analyses (CEAs) alongside randomised controlled trials (RCTs) are increasingly designed to collect resource use and preference-based health status data for the purpose of healthcare technology assessment. However, because of the way these measures are collected, they are prone to missing data, which can ultimately affect the decision of whether an intervention is good value for money. We examine how missing cost and effect outcome data are handled in RCT-based CEAs, complementing a previous review (covering 2003–2009, 88 articles) with a new systematic review (2009–2015, 81 articles) focussing on two different perspectives. First, we provide guidelines on how the information about missingness and related methods should be presented to improve the reporting and handling of missing data. We propose to address this issue by means of a quality evaluation scheme, providing a structured approach that can be used to guide the collection of information, elicitation of the assumptions, choice of methods and considerations of possible limitations of the given missingness problem. Second, we review the description of the missing data, the statistical methods used to deal with them and the quality of the judgement underpinning the choice of these methods. Our review shows that missing data in within-RCT CEAs are still often inadequately handled and the overall level of information provided to support the chosen methods is rarely satisfactory.

Suggested Citation

  • Andrea Gabrio & Alexina J. Mason & Gianluca Baio, 2017. "Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations," PharmacoEconomics - Open, Springer, vol. 1(2), pages 79-97, June.
  • Handle: RePEc:spr:pharmo:v:1:y:2017:i:2:d:10.1007_s41669-017-0015-6
    DOI: 10.1007/s41669-017-0015-6
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    References listed on IDEAS

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. James Altunkaya’s journal round-up for 3rd September 2018
      by jamesaltunkaya in The Academic Health Economists' Blog on 2018-09-03 11:00:24
    2. Thesis Thursday: Andrea Gabrio
      by Chris Sampson in The Academic Health Economists' Blog on 2019-09-19 06:00:59

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    Cited by:

    1. Andrea Gabrio & Catrin Plumpton & Sube Banerjee & Baptiste Leurent, 2022. "Linear mixed models to handle missing at random data in trial‐based economic evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1276-1287, June.
    2. Abualbishr Alshreef & Allan J. Wailoo & Steven R. Brown & James P. Tiernan & Angus J. M. Watson & Katie Biggs & Mike Bradburn & Daniel Hind, 2017. "Cost-Effectiveness of Haemorrhoidal Artery Ligation versus Rubber Band Ligation for the Treatment of Grade II–III Haemorrhoids: Analysis Using Evidence from the HubBLe Trial," PharmacoEconomics - Open, Springer, vol. 1(3), pages 175-184, September.
    3. Ângela Jornada Ben & Johanna M. Dongen & Mohamed El Alili & Martijn W. Heymans & Jos W. R. Twisk & Janet L. MacNeil-Vroomen & Maartje Wit & Susan E. M. Dijk & Teddy Oosterhuis & Judith E. Bosmans, 2023. "The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 951-965, August.

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