论文信息 - An enhanced sign test for dependent binary data with small numbers of clusters

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.

Patrick D. Gerard | William R. Schucany | W. R. Schucany | P. Gerard

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