A study of the effect of dichotomizing ordinal data upon modeling

Abstract

Investigators in health services research often analyze ordinal outcome data as if it were dichotomous by collapsing outcomes into two groups and using standard logistic regression, rather than an alternate discrete model. This paper assesses the degree to which parameters are inaccurately estimated and ower is lost when this is done. Simulations with a five level ordinal outcome variable demonstrated that the loss of precision and power can be very pronounced. Parameter estimates were especially poor in the presence of moderately sparse data, while the loss of power was most evident with balanced data. Polychotomous models are also examined and discussed. An example using length of hospital stay data grouped into discrete outcome levels is given.

Keywords:

• Proportional odds
• separate slopes
• test for homogeneity
• polychotomous model