Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial.
Baptiste Leurent, Manuel Gomes, Rita Faria, Stephen Morris, Richard Grieve, James R Carpenter
Author Information
Baptiste Leurent: Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. baptiste.leurent@lshtm.ac.uk. ORCID
Manuel Gomes: Department of Applied Health Research, University College London, London, UK. ORCID
Rita Faria: Centre for Health Economics, University of York, York, UK.
Stephen Morris: Department of Applied Health Research, University College London, London, UK.
Richard Grieve: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
James R Carpenter: Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
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.
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