Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11408

Title: Pattern-mixture models for categorical outcomes with non-monotone missingness
Authors: Jansen, Ivy
Molenberghs, Geert
Issue Date: 2010
Abstract: Although most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years [R.J.A. Little, Pattern-mixture models for multivariate incomplete data, J. Am. Stat. Assoc. 88 (1993), pp. 125-134; R.J.A. Lrittle, A class of pattern-mixture models for normal incomplete data, Biometrika 81 (1994), pp. 471-483], since it is often argued that selection models, although many are identifiable, should be approached with caution, especially in the context of MNAR models [R.J. Glynn, N.M. Laird, and D.B. Rubin, Selection modeling versus mixture modeling with nonignorable nonresponse, in Drawing Inferences from Self-selected Samples, H. Wainer, ed., Springer-Verlag, New York, 1986, pp. 115-142]. In this paper, the focus is on several strategies to fit pattern-mixture models for non-monotone categorical outcomes. The issue of under-identification in pattern-mixture models is addressed through identifying restrictions. Attention will be given to the derivation of the marginal covariate effect in pattern-mixture models for non-monotone categorical data, which is less straightforward than in the case of linear models for continuous data. The techniques developed will be used to analyse data from a clinical study in psychiatry.
Notes: Molenberghs, G (reprint author) [Jansen, Ivy; Molenberghs, Geert] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/11408
DOI: 10.1080/00949650903062566
ISI #: 000283061500008
ISSN: 0094-9655
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version241.28 kBAdobe PDF
Peer-reviewed author version 244.22 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.