ABSTRACT
In clinical research, the range of a categorical response variable often contains more than two values. Also, the dimension of a categorical variable can often be multivariate. The focus of this chapter is on categorical variables that are non-binary and on the association among the components of a multivariate categorical variable. A contingency table is usually employed to summarize results from multivariate categorical responses, In practice, hypotheses testing for goodness-of-fit, independence (or association), and categorical shift are usually conducted for evaluation of clinical efficacy and safety of a test compound under investigation. For example, a sponsor may be interested in determining whether the test treatment has any influence on the performance of some primary study endpoints, e.g., the presence/absence of a certain event such as disease progression, adverse event, or response (complete/partial) of a cancer tumor. It is then of interest to test the null hypothesis of independence or no association between the test treatment (e.g., before and after treatment) and the change in the study endpoint. In this chapter, formulas for sample size calculation for testing goodness-of-fit and independence (or association) under an r×c contingency table is derived based on various chi-square type test statistics such as Pearson’s chi-square and likelihood ratio test statistics. In addition, procedures for sample size calculation for testing categorical shift using McNemar’s test and/or Stuart-Maxwell test is also derived.
