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Multinomial regression models based on continuation ratios.

C Cox1

  • 1Division of Biostatistics, University of Rochester Medical Center, New York 14642.

Statistics in Medicine
|March 1, 1988
PubMed
Summary
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This study introduces continuation ratio models for analyzing multinomial responses. These models offer independent analysis of specific categories, aiding in understanding complex ordinal data, particularly in pharmaceutical research.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Multinomial response data are common in various scientific fields.
  • Existing logit models may not fully capture nuanced differences in ordinal outcomes.
  • Continuation ratio models offer a structured approach to analyzing ordered categorical data.

Purpose of the Study:

  • To introduce and explore the application of continuation ratio models for multinomial responses.
  • To demonstrate the utility of these models in dissecting effects within specific categories of ordinal variables.
  • To illustrate the fitting of these models to real-world data, including a pharmaceutical study.

Main Methods:

  • Utilizing conditional probabilities within a logit modeling framework.
  • Modeling various continuation ratios separately to achieve asymptotic independence of estimates and test statistics.

Related Experiment Videos

  • Partitioning likelihood ratio statistics for targeted analysis of ordinal response variable categories.

    • Main Results:

    • Separate modeling of continuation ratios yields asymptotically independent estimates and test statistics.
    • This independence facilitates the partitioning of likelihood ratio statistics.
    • Models using shared parameters across ratios effectively estimate global differences.
    • Application to a pharmaceutical study demonstrates practical utility.

    Conclusions:

    • Continuation ratio models provide a flexible framework for analyzing multinomial responses.
    • Different model specifications are adept at capturing distinct types of differences in response distributions.
    • These models enhance the ability to analyze complex ordinal data in fields like pharmaceutical research.