Statistical models for the analysis of ordered categorical data in public health and medical research.

Ruth Etzioni, S. E. Fienberg, Z. Gilula, S. J. Haberman

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In the late 1970s statisticians extended the methods for analysing loglinear and logit models for cross-classified categorical data to incorporate information about the ordinal structure of the categories corresponding to some of the classification variables. In this paper we review one class of such extensions known as association models. We consider association models with and without order restrictions on the parameters and we use these models to answer research questions about several medical examples involving ordered categorical data. We emphasize the interpretation of parameters in the association models and how this relates to the research questions of interest.

Original languageEnglish (US)
Pages (from-to)179-204
Number of pages26
JournalStatistical methods in medical research
Volume3
Issue number2
DOIs
StatePublished - Jan 1 1994
Externally publishedYes

Fingerprint

Ordered Categorical Data
Association Model
Public Health
Statistical Models
Statistical Model
Biomedical Research
Research
Order Restriction
Logit Model
Log-linear Models
Nominal or categorical data
Logistic Models
Model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

Statistical models for the analysis of ordered categorical data in public health and medical research. / Etzioni, Ruth; Fienberg, S. E.; Gilula, Z.; Haberman, S. J.

In: Statistical methods in medical research, Vol. 3, No. 2, 01.01.1994, p. 179-204.

Research output: Contribution to journalArticle

Etzioni, Ruth ; Fienberg, S. E. ; Gilula, Z. ; Haberman, S. J. / Statistical models for the analysis of ordered categorical data in public health and medical research. In: Statistical methods in medical research. 1994 ; Vol. 3, No. 2. pp. 179-204.
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