Center for Health Services Research in Primary Care, VA Medical Center, Durham, NC 27705, USA.
Sleep Med. 2012 Feb;13(2):123-32. doi: 10.1016/j.sleep.2011.09.007. Epub 2011 Dec 14.
Missing data, e.g. patient attrition, are endemic in sleep disorder clinical trials. Common approaches for dealing with this situation include complete-case analysis (CCA) and last observation carried forward (LOCF). Although these methods are simple to implement, they are deeply flawed in that they may introduce bias and underestimate uncertainty, leading to erroneous conclusions. There are alternative principled approaches, however, that are available in statistical software namely mixed-effects models and multiple imputation. In this paper we introduce terminology used to describe different assumptions about missing data. We emphasize that understanding reasons for missingness is a critical step in the analysis process. We describe and implement both linear mixed-effects models and an inclusive multiple imputation strategy for handling missing data in a randomized trial examining sleep outcomes. These principled strategies are compared with "complete-case analysis" and LOCF. These analyses illustrate that methodologies for accommodating missing data can produce different results in both direction and strength of treatment effects. Our goal is for this paper to serve as a guide to sleep disorder clinical trial researchers on how to utilize principled methods for incomplete data in their trial analyses.
在睡眠障碍临床试验中，数据缺失（例如患者流失）是很常见的。处理这种情况的常用方法包括完全案例分析（CCA）和末次观测结转（LOCF）。尽管这些方法实施起来很简单，但它们存在严重的缺陷，因为它们可能会引入偏差并低估不确定性，从而导致错误的结论。然而，在统计软件中还有其他替代的有原则的方法，即混合效应模型和多重插补。在本文中，我们介绍了用于描述缺失数据的不同假设的术语。我们强调，理解缺失数据的原因是分析过程中的关键步骤。我们描述并实施了线性混合效应模型和一种包容性的多重插补策略，以处理一项随机试验中睡眠结果的缺失数据。这些有原则的策略与“完全案例分析”和 LOCF 进行了比较。这些分析表明，处理缺失数据的方法可以在治疗效果的方向和强度上产生不同的结果。我们的目标是，本文可以作为睡眠障碍临床试验研究人员的指南，指导他们如何在试验分析中利用有原则的方法处理不完整数据。
