A general score-independent test for order-restricted inference.

In the analysis of ordered categorical data, the categories are often assigned a set of subjectively chosen order-restricted scores. To overcome the arbitrariness involved in the assignment of the scores, several score-independent tests have been proposed. However, these methods are limited to 2 × K contingency tables, where K is the number of ordered categories. We present an efficiency robust score-independent test that is applicable to more general situations. The test is embedded into a flexible framework for conditional inference and provides a natural generalization of many familiar tests involving ordered categorical data, such as the generalized Cochran-Mantel-Haenszel test for singly or doubly ordered contingency tables, the Page test for randomized block designs and the Tarone-Ware trend test for survival data. The proposed method is illustrated by several numerical examples.