Missing data perspectives of the fluvoxamine data set: A review

Fitting models to incomplete categorical data requires more care than fitti ng models to the complete data counterparts, not only in the setting of mis sing data that are non-randomly missing, but even in the familiar missing a t random setting. Various aspects of this point of view have been considere d in the literature. We review it using data from a multi-centre trial on t he relief of psychiatric symptoms. First, it is shown how the usual expecte d information matrix (referred to as naive information) is biased even unde r a missing at random mechanism. Second, issues that arise under non-random missingness assumptions are illustrated. It is argued that at least some o f these problems can be avoided using contextual information. Copyright (C) 1999 John Wiley & Sons, Ltd.