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Related Experiment Videos

Regression with an ordered categorical response.

T J Hastie1, J L Botha, C M Schnitzler

  • 1AT&T Bell Laboratories, Murray Hill, New Jersey 07974.

Statistics in Medicine
|July 1, 1989
PubMed
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This study reveals that standard regression analysis can mislead when analyzing ordinal osteoporosis data from South African women. The proportional-odds model offers a more accurate approach for this type of medical data.

Area of Science:

  • Medical statistics
  • Epidemiology
  • Radiology

Background:

  • Mseleni joint disease survey in South Africa.
  • Pelvic X-ray scoring used to measure osteoporosis.
  • Ordinal data construction ranging from 0 to 12.

Purpose of the Study:

  • To evaluate the suitability of ordinary regression techniques for ordinal data.
  • To demonstrate the effectiveness of McCullagh's proportional-odds model for ordinal data analysis.
  • To compare proportional-odds models with ordinary and logistic regression.

Main Methods:

  • Analysis of pelvic X-ray scores from women with Mseleni joint disease.
  • Application of ordinary regression techniques.
  • Implementation and demonstration of McCullagh's proportional-odds model.

Related Experiment Videos

  • Demonstration of non-parametric versions of proportional-odds models.

    • Main Results:

    • Evidence suggests ordinary regression analysis yields misleading results for this ordinal data.
    • McCullagh's proportional-odds model provides a more appropriate analysis for ordinal osteoporosis data.
    • Non-parametric models were demonstrated, relaxing linearity assumptions.

    Conclusions:

    • Standard regression methods are inadequate for analyzing ordinal osteoporosis data.
    • The proportional-odds model is a superior method for analyzing ordinal medical data.
    • Non-parametric extensions offer further flexibility in analyzing such data.