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Reference‐based multiple imputation for missing data sensitivity analyses in trial‐based cost‐effectiveness analysis

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  • Baptiste Leurent
  • Manuel Gomes
  • Suzie Cro
  • Nicola Wiles
  • James R. Carpenter

Abstract

Missing data are a common issue in cost‐effectiveness analysis (CEA) alongside randomised trials and are often addressed assuming the data are ‘missing at random’. However, this assumption is often questionable, and sensitivity analyses are required to assess the implications of departures from missing at random. Reference‐based multiple imputation provides an attractive approach for conducting such sensitivity analyses, because missing data assumptions are framed in an intuitive way by making reference to other trial arms. For example, a plausible not at random mechanism in a placebo‐controlled trial would be to assume that participants in the experimental arm who dropped out stop taking their treatment and have similar outcomes to those in the placebo arm. Drawing on the increasing use of this approach in other areas, this paper aims to extend and illustrate the reference‐based multiple imputation approach in CEA. It introduces the principles of reference‐based imputation and proposes an extension to the CEA context. The method is illustrated in the CEA of the CoBalT trial evaluating cognitive behavioural therapy for treatment‐resistant depression. Stata code is provided. We find that reference‐based multiple imputation provides a relevant and accessible framework for assessing the robustness of CEA conclusions to different missing data assumptions.

Suggested Citation

  • Baptiste Leurent & Manuel Gomes & Suzie Cro & Nicola Wiles & James R. Carpenter, 2020. "Reference‐based multiple imputation for missing data sensitivity analyses in trial‐based cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 171-184, February.
  • Handle: RePEc:wly:hlthec:v:29:y:2020:i:2:p:171-184
    DOI: 10.1002/hec.3963
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    References listed on IDEAS

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    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. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    3. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.

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