Cumulative logit modelling for ordinal response variables: applications to biomedical research

Incorrect statistical methods are often used for the analysisof ordinal response data. Such data are frequently summarizedinto mean scores for comparisons, a fallacious practice becauseordinal data are inherently not equidistant. The ubiquitousPearson chi-square test is invalid because it ignores the rankingof ordinal data. Although some of the non-parametric statisticalmethods take into account the ordering of ordinal data, thesemethods do not accommodate statistical adjustment of confoundingor assessment of effect modification, two overriding analyticgoals in virtually all etiologic inference in biology and medicine.The cumulative logit model is eminently suitable for the anlaysisof ordinal response data. This multivariate method not onlyconsiders the ranked order inherent in ordinal response data,but it also allows adjustment of confounding and assessmentof effect modification based on modest sample size. A non-technicalaccount of the cumulative logit model is given and its applicationsare illustrated by two research examples. The SAS programs forthe data analysis of the research examples are available fromthe author.