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Ordinal regression models for epidemiologic data.

B G Armstrong1, M Sloan

  • 1School of Occupational Health, Montreal, Quebec, Canada.

American Journal of Epidemiology
|January 1, 1989
PubMed
Summary
This summary is machine-generated.

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Analyzing health status in epidemiology often simplifies ordinal data. This study explores advanced statistical models, finding they offer similar results to traditional methods when data is appropriately dichotomized.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Health status in epidemiologic studies is typically measured on an ordinal scale.
  • Ordinal data is often reduced to a dichotomy for analysis, losing valuable information.
  • Advanced statistical models exist to fully utilize ordinal response data but are underutilized in epidemiology.

Purpose of the Study:

  • To discuss and evaluate the utility of two statistical models for ordinal response data: the cumulative odds model and the continuation ratio model.
  • To compare the efficiency and interpretability of these models against traditional dichotomization methods in epidemiologic studies.

Main Methods:

  • Discussion of the cumulative odds and continuation ratio models, highlighting their interpretability via odds ratios and ability to handle confounding variables.
  • Evaluation through asymptotic relative efficiency calculations and simulation studies.

Related Experiment Videos

  • Application to a real-world dataset from a study on chest x-rays of workers exposed to mineral fibers.

    • Main Results:

    • Ordinal regression models (cumulative odds, continuation ratio) offer clear assumptions and can be implemented using standard statistical packages.
    • Simple logistic regression on dichotomized data can retain over 75% efficiency of ordinal models if the dichotomy is near the optimal point.
    • Application to mineral fiber exposure data yielded results comparable to conventional dichotomization using simple logistic regression.

    Conclusions:

    • The cumulative odds and continuation ratio models are user-friendly and interpretable for analyzing ordinal health status data in epidemiology.
    • While advanced models are valuable, conventional dichotomization may be sufficiently efficient under specific conditions, offering similar outcomes.