An enhanced sign test for dependent binary data with small numbers of clusters

The classical sign test is proper for hypotheses about a specified success probability, when based on independent trials. For such a hypothesis we introduce a new exact test that is appropriate with clustered binary data. It combines a permutation approach and an exact parametric bootstrap calculation. Simulation studies show it to be superior to a sign test based on aggregated cluster level data. The new test is more powerful than or comparable to a standard permutation test whenever (1) the number of clusters is small or (2) for larger cluster numbers under strong clustering. The results from a chemical repellency trial are used to illustrate three legitimate test methods. (c) 2006 Elsevier B.V. All rights reserved.