Publication
Title
Generalized shared-parameter models and missingness at random
Author
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.
Language
English
Source (journal)
Statistical modelling: an international journal. - Place of publication unknown
Publication
Place of publication unknown : 2011
ISSN
1471-082X
Volume/pages
11:4(2011), p. 279-310
ISI
000293922900002
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 28.11.2011
Last edited 16.07.2017
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