Categorical data often occur in clinical trials. For example, adverse events may be classified on an ordinal scale as mild, moderate or severe. In this chapter we will primarily consider the analysis of measurements made on ordered categorical scales; however, we also describe how unordered categorical data can be analysed. A fixed effects method for analysing ordinal data known as 'ordinal logistic regression' was first suggested by McCullagh (1980) and has been widely applied. The mixed categorical model is far less well established. The model we will define will be based on extending ordinal logistic regression to include random effects and covariance patterns. As we suggested in Chapter 3 for GLMMs, some readers with a less statistical background may wish to read only the introductory paragraphs of each section which will enable them to identify where these methods might prove useful. The final section of this chapter and sections of subsequent chapters will illustrate the application of mixed categorical models.
Ordinal logistic regression will be described in Section 4.1. It is extended to a mixed ordinal logistic regression model in Section 4.2. In Section 4.3 we describe how the model can be adapted to analyse unordered categorical data. In Section 4.4 some practical issues related to model fitting and interpretation are considered, and a worked example is given in Section 4.5.
Was this article helpful?