Linear mixed models to handle missing at random data in trial-based economic evaluations.
Linear mixed models to handle missing at random data in trial-based economic evaluations.
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DOI：
10.1002/hec.4510
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发表时间：
2022-06
期刊：
影响因子：
2.1
通讯作者：
Leurent, Baptiste
中科院分区：
文献类型：
--
作者：
Gabrio, Andrea;Plumpton, Catrin;Banerjee, Sube;Leurent, Baptiste
基于试验的成本效益分析（CEA）是评估卫生干预措施的重要证据来源。在这些研究中，通常在多个时间点测量成本和效果结果，但可能会缺失一些观察结果。将分析限制在具有完整数据的参与者可能会导致有偏见和效率低下的估计。推荐使用多重插补等方法，因为它们可以更好地利用可用数据，并且在限制性较低的随机缺失（MAR）假设下有效。线性混合效应模型（Linear mixed effects models，LIFE）提供了一种简单的替代方法来处理MAR下的缺失数据，而无需插补，并且在CEA背景下尚未得到很好的探索。在本文中，我们的目标是让读者熟悉Linux并展示其在CEA中的实现。我们说明了抗抑郁药的随机试验的方法，并提供了R和Stata的实现代码。我们希望，与其他缺失数据方法相比，与LIFE相关的更熟悉的统计框架将鼓励它们的实施，并使从业者远离不适当的方法。
Trial‐based cost‐effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods.
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