Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial
Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial
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DOI：
10.1007/s40273-018-0650-5
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发表时间：
2018-08-01
影响因子：
4.4
通讯作者：
Carpenter, James R.
中科院分区：
文献类型：
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作者：
Leurent, Baptiste;Gomes, Manuel;Carpenter, James R.
随机对照试验的成本效益分析（CEA）是医疗保健决策者的关键信息来源。然而，数据缺失是一个常见的问题，可能严重损害其有效性。一个主要的问题是，数据丢失的机会可能直接与未观察到的值本身有关[非随机丢失（MNAR）]。例如，健康状况较差的患者可能不太可能完成生活质量问卷。然而，从现有数据无法确定这种情况的程度。指南建议进行敏感性分析，以评估结论对合理MNAR假设的稳健性，但实际上很少这样做，可能是因为缺乏实际指导。本教程旨在通过提供一个可访问的框架和实用指南来解决这个问题，以便在基于试验的CEA中对MNAR数据进行敏感性分析。我们回顾了一些进行敏感性分析的方法，但重点放在一个特别容易的方法，其中的数据是多重插补，然后修改，以反映合理的MNAR方案。我们说明了这种方法的实施减肥试验，提供的软件代码。然后，我们将围绕其在实践中的使用进一步探讨问题。
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.