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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Related Experiment Video

Updated: Jul 3, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Fitting stratified proportional odds models by amalgamating conditional likelihoods.

Bhramar Mukherjee1, Jaeil Ahn, Ivy Liu

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48103, USA. bhramar@umich.edu

Statistics in Medicine
|July 12, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing stratified ordinal logistic regression models. The approach effectively handles complex data structures in biomedical research, offering a practical solution for varying intercepts.

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Last Updated: Jul 3, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Classical methods for logistic regression with varying intercepts struggle with stratified data and multiple ordered categories.
  • Existing techniques fail to adequately address nuisance parameters in K-category cumulative logit models (K>2) with stratum-specific intercepts.

Purpose of the Study:

  • To develop a novel methodology for fitting stratified proportional odds models with varying intercepts.
  • To provide a robust and easily implementable solution for analyzing complex ordinal outcome data in stratified settings.

Main Methods:

  • Proposing a method that amalgamates conditional likelihoods from all binary collapsings of the ordinal scale.
  • Incorporating categorical and continuous covariates within a general regression framework.
  • Developing a robust sandwich estimate for the variance of the proposed estimator.

Main Results:

  • Demonstrating equivalence of the proposed approach to existing estimators for binary exposures.
  • Illustrating the method's applicability through three real-world biomedical research examples.
  • Presenting simulation results that compare the new method with random effects models.

Conclusions:

  • The proposed methodology offers a practical and easily implementable solution for stratified proportional odds models with varying intercepts.
  • The approach is robust and suitable for analyzing complex ordinal data in various research fields, particularly in biomedical studies.
  • The method overcomes limitations of classical conditioning techniques for general K-category cumulative logit models.