Mixed multivariate generalized linear models for assessing lower-limb arterial stenoses

Cengiz M. A., Percy D. F.

Statistics in Medicine, vol.20, no.11, pp.1663-1679, 2001 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 11
  • Publication Date: 2001
  • Doi Number: 10.1002/sim.924
  • Journal Name: Statistics in Medicine
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1663-1679
  • Ondokuz Mayıs University Affiliated: Yes


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.