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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11484

Title: Arbitrariness of models for augmented and coarse data, with emphasis on incomplete data and random effects models
Authors: Verbeke, Geert
Molenberghs, Geert
Issue Date: 2010
Citation: STATISTICAL MODELLING, 10 (4). p. 391-419
Abstract: Statistical models often extend beyond the data available. First, in coarse data, what is actually observed is less detailed than what might be, owing to incompleteness, censoring, grouping, or a combination thereof. Second, in augmented data, the observed data are hypothetically supplemented with random effects, latent variables/classes, or component membership in mixture distributions. The two settings together will be referred to as enriched data. Reasons for modelling enriched data encompass mathematical and computational convenience, advantages in interpretation, and substantive plausibility. Models for enriched data combine evidence coming from empirical data with unverifiable model components, resting entirely on assumptions. This has acute consequences for enriched data, but knowledge about this issue is somewhat scattered. We provide a unified framework for enriched data and show, generally and with focus on incomplete-data models and random-effects models on the other hand, that to any given model an entire class of models can be assigned, with all of its members producing the same fit to the observed data but arbitrary regarding the unobservable parts of the enriched data. The concepts developed are illustrated using a clinical trial in toenail dermatophyte onychomycosis and a developmental toxicity study conducted in mice.
Notes: Molenberghs, G (reprint author) [Verbeke, Geert; Molenberghs, Geert] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Verbeke, Geert; Molenberghs, Geert] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. geert.molenberghs@uhasselt.be geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/11484
DOI: 10.1177/1471082X0901000403
ISI #: 000284229600003
ISSN: 1471-082X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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