Missing data perspectives of the fluvoxamine data set: a review.
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.
Molenberghs
G
G
Biostatistics, Limburgs Universitair Centrum, B3590 Diepenbeek, Belgium. geert.molenberghs@luc.ac.be
Goetghebeur
E J
EJ
Lipsitz
S R
SR
Kenward
M G
MG
Lesaffre
E
E
Michiels
B
B
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
D016666:Fluvoxamine; D017367:Selective Serotonin Reuptake Inhibitors