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Regression models for unconstrained, partially or fully constrained continuation odds ratios.

S R Cole1, C V Ananth

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD 21205, USA. scole@jhsph.edu

International Journal of Epidemiology
|February 1, 2002
PubMed
Summary

Epidemiologists can now fit flexible ordered response models using a novel "person-threshold" data structure. This approach allows for unconstrained or partially constrained odds ratios, overcoming limitations of standard continuation ratio models.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Ordered response data is common in epidemiological studies.
  • Standard continuation ratio models impose restrictive assumptions on exposure effects across response thresholds.

Purpose of the Study:

  • To present a flexible method for fitting ordered response regression models.
  • To allow for unconstrained, partially constrained, or fully constrained continuation odds ratios.

Main Methods:

  • A 'person-threshold' data set is created, with each record representing a subject at risk for a specific response threshold.
  • Standard binary logistic regression is applied to this restructured data to estimate the continuation-ratio model.
  • The method accommodates unconstrained, partially constrained, and fully constrained odds ratios.

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Main Results:

  • The 'person-threshold' approach enables estimation of more flexible continuation-ratio models.
  • A simulation study provides insights into the properties of this novel method.
  • An illustrative example demonstrates the practical application of the models.

Conclusions:

  • The 'person-threshold' method offers a valuable alternative for analyzing ordered responses in epidemiology.
  • This approach overcomes the homogeneity constraints of traditional models.
  • The paper discusses statistical software for implementing the proposed methodology.