Modelling bivariate ordinal responses smoothly with examples from ophthalmology and genetics
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Modelling bivariate ordinal responses smoothly with examples from ophthalmology and genetics. / Bustami, R; Lesaffre, E; Molenberghs, G; Loos, R; Danckaerts, M; Vlietinck, R.
I: Statistics in Medicine, Bind 20, Nr. 12, 30.06.2001, s. 1825-42.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Modelling bivariate ordinal responses smoothly with examples from ophthalmology and genetics
AU - Bustami, R
AU - Lesaffre, E
AU - Molenberghs, G
AU - Loos, R
AU - Danckaerts, M
AU - Vlietinck, R
N1 - Copyright 2001 John Wiley & Sons, Ltd.
PY - 2001/6/30
Y1 - 2001/6/30
N2 - 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.
AB - 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.
KW - Adolescent
KW - Child
KW - Child Behavior/physiology
KW - Diabetic Retinopathy/epidemiology
KW - Female
KW - Humans
KW - Likelihood Functions
KW - Logistic Models
KW - Male
KW - Models, Biological
KW - Models, Genetic
KW - Models, Statistical
KW - Risk Factors
KW - Statistics, Nonparametric
KW - Twin Studies as Topic
U2 - 10.1002/sim.793
DO - 10.1002/sim.793
M3 - Journal article
C2 - 11406844
VL - 20
SP - 1825
EP - 1842
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 12
ER -
ID: 258040802