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Missing data perspectives of the fluvoxamine data set: a review
Author(s) -
Molenberghs Geert,
Goetghebeur Els J. T.,
Lipsitz Stuart R.,
Kenward Michael G.,
Lesaffre Emmanuel,
Michiels Bart
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990915/30)18:17/18<2449::aid-sim268>3.0.co;2-w
Subject(s) - missing data , categorical variable , data set , computer science , fluvoxamine , set (abstract data type) , statistics , data mining , artificial intelligence , mathematics , machine learning , medicine , receptor , serotonin , fluoxetine , programming language
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. Copyright © 1999 John Wiley & Sons, Ltd.