title: Missing Data in Longitudinal Trials - Part B, Analytic Issues.
abstract: 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.
authors: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago.
authors: Juned
authors: J
authors: Siddique
authors: C Hendricks
authors: CH
authors: Brown
authors: Donald
authors: D
authors: Hedeker
authors: Naihua
authors: N
authors: Duan
authors: Robert D
authors: RD
authors: Gibbons
authors: Jeanne
authors: J
authors: Miranda
authors: Philip W
authors: PW
authors: Lavori