title: Missing data perspectives of the fluvoxamine data set: a review.
abstract: Fitting models to incomplete categorical data requires more care than fitting 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.
authors.affiliation: Biostatistics, Limburgs Universitair Centrum, B3590 Diepenbeek, Belgium. geert.molenberghs@luc.ac.be
authors.forename: G
authors.initials: G
authors.lastname: Molenberghs
authors.forename: E J
authors.initials: EJ
authors.lastname: Goetghebeur
authors.forename: S R
authors.initials: SR
authors.lastname: Lipsitz
authors.forename: M G
authors.initials: MG
authors.lastname: Kenward
authors.forename: E
authors.initials: E
authors.lastname: Lesaffre
authors.forename: B
authors.initials: B
authors.lastname: Michiels
mesh_terms: D003627:Data Interpretation, Statistical; D016666:Fluvoxamine / Q000009:adverse effects / Q000627:therapeutic use*; D006801:Humans; D016013:Likelihood Functions; D001523:Mental Disorders / Q000188:drug therapy*; D008954:Models, Biological; D015337:Multicenter Studies as Topic; D010352:Patient Dropouts; D017367:Selective Serotonin Reuptake Inhibitors / Q000009:adverse effects / Q000627:therapeutic use*; D016896:Treatment Outcome
chemical_list: D016666:Fluvoxamine; D017367:Selective Serotonin Reuptake Inhibitors