Siddique Juned, Brown C Hendricks, Hedeker Donald, Duan Naihua, Gibbons Robert D, Miranda Jeanne, Lavori Philip W
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago.
Psychiatr Ann. 2008 Dec 1;38(12):793-801. doi: 10.3928/00485713-20081201-09.
Longitudinal designs in psychiatric research have many benefits, including the ability to measure the course of a disease over time. However, measuring participants repeatedly over time also leads to repeated opportunities for missing data, either through failure to answer certain items, missed assessments, or permanent withdrawal from the study. To avoid bias and loss of information, one should take missing values into account in the analysis. Several popular ways that are now being used to handle missing data, such as the last observation carried forward (LOCF), often lead to incorrect analyses. We discuss a number of these popular but unprincipled methods and describe modern approaches to classifying and analyzing data with missing values. We illustrate these approaches using data from the WECare study, a longitudinal randomized treatment study of low income women with depression.
精神病学研究中的纵向设计有诸多益处，包括能够随时间推移测量疾病的进程。然而，随着时间的推移对参与者进行反复测量也会导致出现多次数据缺失的情况，原因可能是未能回答某些项目、错过评估或者永久性退出研究。为避免偏差和信息丢失，在分析中应考虑缺失值。目前用于处理缺失数据的几种常用方法，比如末次观察结转（LOCF），往往会导致分析结果不正确。我们讨论了一些这类常用但不合理的方法，并描述了对带有缺失值的数据进行分类和分析的现代方法。我们使用WECare研究的数据来说明这些方法，该研究是一项针对低收入抑郁症女性的纵向随机治疗研究。
