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A bivariate cumulative probit regression model for ordered categorical data

K Kim1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115-6084, USA.

Statistics in Medicine
|June 30, 1995
PubMed
Summary
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This study introduces a new regression model for analyzing paired categorical health data, extending existing methods to better understand risk factors in conditions like diabetic retinopathy.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Ophthalmology

Background:

  • Bivariate ordered categorical data analysis is crucial in clinical research, particularly for paired organs.
  • Existing models like the bivariate probit model are limited to dichotomous outcomes.
  • Ophthalmological studies often involve ordered categorical data with distinct covariates for each paired organ.

Purpose of the Study:

  • To propose a novel latent variable regression model for bivariate ordered categorical data.
  • To extend the standard bivariate probit model to accommodate outcomes with more than two categories.
  • To incorporate different covariates for each margin, reflecting common clinical study designs.

Main Methods:

  • Development of a latent variable regression model for bivariate ordered categorical data.

Related Experiment Videos

  • Extension of the bivariate probit model to handle multi-category outcomes.
  • Utilizing stochastic ordering and the bivariate normal distribution's correlation coefficient for intra-subject dependency.
  • Parameter estimation via a numerical procedure.

    • Main Results:

    • The proposed model effectively handles bivariate ordered categorical data.
    • It successfully incorporates distinct covariates for each margin.
    • Demonstrates utility in identifying risk factors for diabetic retinopathy using real-world data.

    Conclusions:

    • The proposed latent variable regression model offers a flexible and powerful tool for analyzing bivariate ordered categorical data in clinical and epidemiological studies.
    • This methodology is particularly valuable for research involving paired organs, such as in ophthalmology.
    • The model facilitates the identification of risk factors and understanding of intra-subject dependency.