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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24956

Title: Multinomial additive hazard model to assess the disability burden using cross-sectional data
Authors: Yokota, Renata T. C.
Van Oyen, Herman
Looman, Caspar W. N.
Nusselder, Wilma J.
Otava, Martin
Kifle, Yimer Wasihun
Molenberghs, Geert
Issue Date: 2017
Publisher: WILEY
Citation: BIOMETRICAL JOURNAL, 59(5), p. 901-917
Abstract: Population aging is accompanied by the burden of chronic diseases and disability. Chronic diseases are among the main causes of disability, which is associated with poor quality of life and high health care costs in the elderly. The identification of which chronic diseases contribute most to the disability prevalence is important to reduce the burden. Although longitudinal studies can be considered the gold standard to assess the causes of disability, they are costly and often with restricted sample size. Thus, the use of cross-sectional data under certain assumptions has become a popular alternative. Among the existing methods based on cross-sectional data, the attribution method, which was originally developed for binary disability outcomes, is an attractive option, as it enables the partition of disability into the additive contribution of chronic diseases, taking into account multimorbidity and that disability can be present even in the absence of disease. In this paper, we propose an extension of the attribution method to multinomial responses, since disability is often measured as a multicategory variable in most surveys, representing different severity levels. The R function constrOptim is used to maximize the multinomial log-likelihood function subject to a linear inequality constraint. Our simulation study indicates overall good performance of the model, without convergence problems. However, the model must be used with care for populations with low marginal disability probabilities and with high sum of conditional probabilities, especially with small sample size. For illustration, we apply the model to the data of the Belgian Health Interview Surveys.
Notes: [Yokota, Renata T. C.; Van Oyen, Herman] Sci Inst Publ Hlth, Dept Publ Hlth & Surveillance, B-1050 Brussels, Belgium. [Yokota, Renata T. C.] Vrije Univ Brussel, Interface Demog, Dept Sociol, Brussels, Belgium. [Van Oyen, Herman] Univ Ghent, Dept Publ Hlth, Ghent, Belgium. [Looman, Caspar W. N.; Nusselder, Wilma J.] Erasmus MC, Dept Publ Hlth, Rotterdam, Netherlands. [Otava, Martin] Janssen Pharmaceut, Beerse, Belgium. [Kifle, Yimer Wasihun; Molenberghs, Geert] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Diepenbeek, Belgium. [Kifle, Yimer Wasihun] Univ Antwerp, Vaccine & Infect Dis Inst, Ctr Hlth Econ Res & Modeling Infect Dis, Antwerp, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, Interuniv Inst Biostatist & Stat Bioinformat I Bi, Leuven, Belgium.
URI: http://hdl.handle.net/1942/24956
DOI: 10.1002/bimj.201600157
ISI #: 000408988700014
ISSN: 0323-3847
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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