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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/382

Title: Modelling bivariate ordinal responses smoothly with examples from ophthalmology and genetics
Authors: Bustami, Rami
Lesaffre, Emmanuel
Molenberghs, Geert
Loos, Ruth
Danckaerts, Marina
Vlietinck, Robert
Keywords: Categorical data
Non and semiparametric methods
Multivariate data
Issue Date: 2001
Publisher: JOHN WILEY
Citation: Statistics in Medicine, 20(12). p. 1825-1842
Abstract: A non-parametric implementation of the bivariate Dale model (BDM) is presented as an extension of the generalized additive model (GAM) of Hastie and Tibshirani. The original BDM is an example of a bivariate generalized linear model. In this paper smoothing is introduced on the marginal as well as on the association level. Our non-parametric procedure can be used as a diagnostic tool for identifying parametric transformations of the covariates in the linear BDM, hence it also provides a kind of goodness-of-fit test for a bivariate generalized linear model. Cubic smoothing spline functions for the covariates are estimated by maximizing a penalized version of the log-likelihood. The method is applied to two studies. The first study is the classical Wisconsin Epidemiologic Study of Diabetic Retinopathy. The second study is a twin study, where the association between the elements of twin pairs is of primary interest. The results show that smoothing on the association level can give a significant improvement to the model fit.
URI: http://hdl.handle.net/1942/382
DOI: 10.1002/sim.793
ISI #: 000169422100008
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2002
Appears in Collections: Research publications

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