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Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial

Author

Listed:
  • Baptiste Leurent

    () (London School of Hygiene and Tropical Medicine)

  • Manuel Gomes

    (University College London)

  • Rita Faria

    (University of York)

  • Stephen Morris

    (University College London)

  • Richard Grieve

    (London School of Hygiene and Tropical Medicine)

  • James R. Carpenter

    (London School of Hygiene and Tropical Medicine
    MRC Clinical Trials Unit at University College London)

Abstract

Abstract Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice.

Suggested Citation

  • Baptiste Leurent & Manuel Gomes & Rita Faria & Stephen Morris & Richard Grieve & James R. Carpenter, 2018. "Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial," PharmacoEconomics, Springer, vol. 36(8), pages 889-901, August.
  • Handle: RePEc:spr:pharme:v:36:y:2018:i:8:d:10.1007_s40273-018-0650-5
    DOI: 10.1007/s40273-018-0650-5
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    References listed on IDEAS

    as
    1. Andrew Briggs & Taane Clark & Jane Wolstenholme & Philip Clarke, 2003. "Missing.... presumed at random: cost-analysis of incomplete data," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 377-392.
    2. Elisabeth Fenwick & Bernie J. O'Brien & Andrew Briggs, 2004. "Cost-effectiveness acceptability curves - facts, fallacies and frequently asked questions," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 405-415.
    3. Rita Faria & Manuel Gomes & David Epstein & Ian White, 2014. "A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials," PharmacoEconomics, Springer, vol. 32(12), pages 1157-1170, December.
    4. 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.
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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

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