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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/416

Title: Pattern-mixture models with proper time dependence
Authors: Kenward, Michael G.
Molenberghs, Geert
Thijs, Herbert
Keywords: Longitudinal data
Missing data
Issue Date: 2003
Citation: Biometrika, 90(1). p. 53-71
Abstract: Recently, pattern-mixture modelling has become a popular tool for modelling incomplete longitudinal data.Such models are under-identified in the sense that, for any drop-out pattern, the data provide no direct information on the distribution of the unobserved outcomes, given the observed ones. One simple way of overcoming this problem, ordinary extrapolation of sufficiently simple pattern-specific models, often produces rather unlikely descriptions; several authors consider identifying restrictions instead. Molenberghs et al. (1998) have constructed identifying restrictions corresponding to missing at random. In this paper, the family of restrictions where drop-out does not depend on future, unobserved observations is identified. The ideas are illustrated using a clinical study of Alzheimer patients
URI: http://hdl.handle.net/1942/416
DOI: 10.1093/biomet/90.1.53
ISI #: 000181996800005
ISSN: 0006-3444
Category: A1
Type: Journal Contribution
Validation: ecoom, 2004
Appears in Collections: Research publications

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