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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/358

Title: Parametric models for incomplete continuous and categorical longitudinal studies data
Authors: Kenward, Michael G.
Molenberghs, Geert
Keywords: Categorical data
Longitudinal data
Missing data
Issue Date: 1999
Publisher: ARNOLD
Citation: Statistical Methods in Medical Research, 8(1). p. 51-83
Abstract: This paper reviews models for incomplete continuous and categorical longitudinal data. In terms of Rubin's classification of missing value processes we are specifically concerned with the problem of nonrandom missingness. A distinction is drawn between the classes of selection and pattern-mixture models and, using several examples, these approaches are compared and contrasted. The central roles of identifiability and sensitivity are emphasized throughout.
URI: http://hdl.handle.net/1942/358
DOI: 10.1177/096228029900800105
ISI #: 000083699900005
ISSN: 0962-2802
Type: Journal Contribution
Validation: ecoom, 2000
Appears in Collections: Research publications

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