Missing data perspectives of the fluvoxamine data set: a review

✍ Scribed by Geert Molenberghs; Els J. T. Goetghebeur; Stuart R. Lipsitz; Michael G. Kenward; Emmanuel Lesaffre; Bart Michiels

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 135 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2449::aid-sim268>3.0.co;2-w

✦ Synopsis

Fitting models to incomplete categorical data requires more care than "tting models to the complete data counterparts, not only in the setting of missing data that are non-randomly missing, but even in the familiar missing at random setting. Various aspects of this point of view have been considered in the literature. We review it using data from a multi-centre trial on the relief of psychiatric symptoms. First, it is shown how the usual expected information matrix (referred to as naive information) is biased even under a missing at random mechanism. Second, issues that arise under non-random missingness assumptions are illustrated. It is argued that at least some of these problems can be avoided using contextual information.