Title: Modelling bivariate ordinal responses smoothly with examples from ophthalmology and genetics
Authors: Bustami, R ×
Lesaffre, Emmanuel
Molenberghs, G
Loos, Ruth
Danckaerts, Marina
Vlietinck, Robert #
Issue Date: Jun-2001
Series Title: Statistics in medicine. vol:20 issue:12 pages:1825-42
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.
ISSN: 0277-6715
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat)
Clinical Genetics Section (-)
Exercise Physiology Research Group
Research Group Psychiatry
× corresponding author
# (joint) last author

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