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.Electronic supplementary materialThe online version of this article (10.1007/s40273-018-0650-5) contains supplementary material, which is available to authorized users.
Highlights
Cost-effectiveness analyses (CEA) of randomised trials are an important source of information to help decide which health care programmes to provide
A recent review has found that, in practice, cost-effectiveness studies rarely conduct such a sensitivity analysis [7]. We discussed this issue with stakeholders, and an important barrier that was identified was the lack of software tools and guidance to conduct these analyses
While any of the methods above would allow an appropriate assessment of departures from missing at random’ (MAR), we will focus on the pattern-mixture approach in the remainder of this paper because (1) it allows for more interpretable parameters, making this approach more accessible and transparent; (2) it seems to be the main approach currently used in clinical trial sensitivity analysis [7, 34]; (3) our discussion with stakeholders confirmed this approach was appealing in the CEA context; and (4) pattern-mixture models can be implemented using standard missing data methods, such as multiple imputation (MI), and build naturally on the MAR analysis, as we will see below
Summary
Cost-effectiveness analysis of randomised trials with missing data should assess the robustness of their findings to possible departures from the missing at random assumption. Multiple imputation provides a flexible and accessible framework to conduct these sensitivity analyses. Sensitivity analysis results should be reported in a transparent way, allowing decision-makers to assess the plausibility of their respective assumptions
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