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Mixed multivariate generalized linear models for assessing lower‐limb arterial stenoses
Author(s) -
Cengiz Mehmet A.,
Percy David F.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.924
Subject(s) - multivariate statistics , context (archaeology) , observational study , generalized linear model , multivariate analysis , linear model , statistics , linear regression , binary number , generalized linear mixed model , computer science , observable , general linear model , mathematics , econometrics , biology , paleontology , physics , arithmetic , quantum mechanics
Experiments and observational studies often involve gathering information on several response variables, enabling us to model their dependence on observable predictor variables. Despite associations between the response variables, they are often analysed separately using general and generalized linear models. This paper investigates applications of multivariate regression analysis to improve the accuracy of predictions and decisions, in the specific context of diagnosing arterial stenoses in human legs. Two basic models are developed for this application, using (i) four binary responses and (ii) a mixture of two binary and two normal responses. The results clearly demonstrate the potential advantages offered by this approach. Copyright © 2001 John Wiley & Sons, Ltd.