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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/352

Title: Missing data perspectives of the fluvoxamine data set: a review
Authors: Molenberghs, Geert
Goetghebeur, Els
Lipsitz, Stuart
Kenward, Michael G.
Lesaffre, Emmanuel
Michiels, Bart
Keywords: Missing data
Longitudinal data
Clustered data
Categorical data
Issue Date: 1999
Publisher: JOHN WILEY
Citation: Statistics in Medicine, 18(17-18). p. 2449-2464
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.
URI: http://hdl.handle.net/1942/352
DOI: 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2449::AID-SIM268>3.0.CO;2-W
ISI #: 000082507800021
ISSN: 0277-6715
Type: Journal Contribution
Validation: ecoom, 2000
Appears in Collections: Research publications

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