Statistical assessment of ordinal outcomes in comparative studies

Abstract

Ordinal regression is a relatively new statistical method developed for analyzing ranked outcomes. In the past, ranked scales have often been analyzed without making full use of the ordinality of the data or, alternatively, by assigning arbitrary numerical scores to the ranks. While ordinal regression models are now available to make full use of ranked data, they are not used widely. This article, directed to clinical researchers and epidemiologists, provides a description of the properties of these methods. Using ordinal measures of back pain in a follow-up study of adolescent idiopathic scoliosis, we illustrate the advantages of these methods and describe how to interpret the estimated parameters. Comparisons with binary logistic regression are made to show why a single dichotomization of the ordinal scale may lead to incorrect inferences. Two ordinal models (the proportional odds and the continuation ratio models) are discussed, and the goodness-of-fit of these models is examined. We conclude that ordinal regression is a tool that is powerful, simple to use, and produces an interpretable parameter that summarizes the effect between groups over all levels of the outcome.