Title
Generalized shared-parameter models and missingness at random
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Faculty of Medicine and Health Sciences
Publication type
article
Publication
,
Subject
Mathematics
Source (journal)
Statistical modelling: an international journal. - Place of publication unknown
Volume/pages
11(2011) :4 , p. 279-310
ISSN
1471-082X
ISI
000293922900002
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
When data are incomplete, models are often catalogued according to one of the three modelling frameworks to which they belong: selection models (SeM), pattern-mixture models (PMM) and shared-parameter models (SPM). The missing data mechanism is conventionally classified as missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Under MCAR, measurement and missingness mechanism are independent, but that is not the case for MAR. The definition of MAR is in SeM terms. Molenberghs et al. (1998) provided a characterization for PMM. Here, MAR is characterized in the SPM framework, using an extended SPM class. A subfamily, satisfying the MAR condition, is studied in detail. Particular implications for non-monotone missingness as well as for longitudinal data subject to dropout are studied. It is indicated how SPM can be constrained such that dropout at a given point in time can depend on current and past, but not on future measurements. Although, a natural requirement, it is less easily imposed in the PMM and SPM frameworks than in the SeM case. Some of the models proposed are illustrated using a clinical trial in toenail dermatophyte onychomycosis.
E-info
https://repository.uantwerpen.be/docman/iruaauth/083772/02e80caae32.pdf
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