论文标题
线性混合模型在基于试验的经济评估中处理随机数据的丢失
Linear mixed models to handle missing at random data in trial-based economic evaluations
论文作者
论文摘要
基于试验的成本效益分析（CEAS）是评估健康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有效性结果，但可能缺少一些观察结果。将分析限制在具有完整数据的参与者中可能导致偏见和效率低下的估计。建议使用诸如多重插补的方法，因为它们可以更好地利用可用的数据，并且在随机（MAR）假设的限制性较低的情况下有效。线性混合效应模型（LMMS）提供了一种简单的替代方法，可以在不需要的情况下处理MAR下的缺失数据，并且在CEA上下文中探讨了不太详尽。在本手稿中，我们旨在使读者熟悉LMM，并演示其在CEA中的实现。我们说明了抗抑郁药随机试验的方法，并在R和Stata中提供了实施代码。我们希望与其他缺少的数据方法相比，与LMM相关的更熟悉的统计框架将鼓励他们的实施，并使从业者远离方法不足。
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 familiarise readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomised trial of antidepressant, 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.
