Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis
Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis
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DOI：
10.1002/hec.3963
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发表时间：
2019-12-17
期刊：
影响因子：
2.1
通讯作者：
Carpenter, James R.
中科院分区：
文献类型：
--
作者：
Leurent, Baptiste;Gomes, Manuel;Carpenter, James R.
在成本效益分析（CEA）和随机试验中，数据缺失是一个常见的问题，通常假设数据“随机缺失”。然而，这种假设常常是有问题的，并且需要进行敏感性分析来评估随机偏离缺失的影响。基于参考的多重输入为进行此类敏感性分析提供了一种有吸引力的方法，因为通过参考其他试验组，缺失数据假设以直观的方式构建。例如，在安慰剂对照试验中，一个看似合理的非随机机制是，假设实验组中退出的参与者停止接受治疗，结果与安慰剂组的参与者相似。鉴于该方法在其他领域的应用越来越广泛，本文旨在扩展和说明基于参考的多重插值方法在CEA中的应用。介绍了基于参考的归责原则，并提出了对CEA上下文的扩展。该方法在评估认知行为疗法治疗难治性抑郁症的CoBalT试验的CEA中得到说明。提供了状态代码。我们发现，基于参考的多重插值为评估CEA结论对不同缺失数据假设的稳健性提供了一个相关且可访问的框架。
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.