Printed In US.A A BRIEF ORIGINAL CONTRIBUTION Collapsing Ordered Outcome Categories: A Note of Concern

When analyzing and interpreting data from an epidemiologic study where ordinal (ordered categorical) outcomes have been measured in different exposure groups, an effect parameter of interest is the common odds ratio implied by the proportional odds model. This model can sometimes be applied to a collapsed outcome variable, instead of the measured variable, without reducing efficiency considerably. However, in a given data set, changing the outcome categories can affect the effect estimate as well as the inference being drawn from the data, even if the true effect itself has not changed. In particular, one should be careful in dichotomizing the measured outcome variable. Am J Epidemiol 1996;144:421-4. data interpretation, statistical; epidemiologic methods; statistics There are several situations in which ordinal (or-dered categorical) outcome data arise naturally. Even if the underlying outcome variable is continuous, it may not be observable. For example, "degree of pain" is suited to be defined as an ordinal variable. Another, more specific, example is the profusion of small opac-ities on a chest radiograph, which is an outcome o