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

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

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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.
Original languageEnglish
Pages (from-to)171-184
Number of pages14
JournalHealth Economics
Volume29
Issue number2
Early online date17 Dec 2019
DOIs
Publication statusPublished - 1 Feb 2020

Keywords

  • controlled imputation
  • cost-effectiveness analysis
  • missing data
  • missing not at random
  • multiple imputation
  • randomised trial
  • reference-based
  • sensitivity analysis

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