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.