Proportional odds logistic regression--effective means of dealing with limited uncertainty in dichotomizing clinical outcomes

Stat Med. 2006 Dec 30;25(24):4227-34. doi: 10.1002/sim.2678.


Classifying a measurable clinical outcome as a dichotomous variable often involves difficulty with borderline cases that could fairly be assigned either of the two binary class memberships. In such situations the indicated class membership is often highly subjective and subject to, for instance, a measurement error. In other situations the intermediate level of a three-level ordinal factor may sometimes be explicitly reserved for cases which could likely belong to either of the two binary classes. Such indefinite readings are often eliminated from the statistical analysis. In this article we review conceptual and methodological aspects of employing proportional odds logistic regression for a three level ordinal factor as a suitable alternative to ordinary logistic regression when dealing with limited uncertainty in classifying clinical outcome as a binary variable.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Atherosclerosis / diagnostic imaging
  • Blood Glucose / metabolism
  • Calcium / blood
  • Cholesterol / blood
  • Czech Republic
  • Data Interpretation, Statistical*
  • Female
  • Humans
  • Logistic Models*
  • Models, Statistical*
  • Predictive Value of Tests
  • Smoking
  • Ultrasonography


  • Blood Glucose
  • Cholesterol
  • Calcium