NDLI: Exact-Permutation-Based Sign Tests for Clustered Binary Data Via Weighted and Unweighted Test Statistics

Content Provider	Springer Nature Link
Author	McDonald, Janie Gerard, Patrick D. McMahan, Christopher S. Schucany, William R.
Copyright Year	2016
Abstract	Clustered binary data occur frequently in many application areas. When analyzing data of this form, ignoring key features, such as the intracluster correlation, may lead to inaccurate inference, e.g., inflated Type I error rates. For clustered binary data, Gerard and Schucany (Comput Stat Data Anal 51:4622–4632, 2007) proposed an exact test for examining whether the marginal probability of a response differs from 0.5, which is the null hypothesis considered in the classic sign test. This new test maintains the specified Type I error rate and has more power, when compared to both the classic sign and permutation tests. The test statistic proposed by these authors equally weights the observed data from each cluster, regardless of whether the clusters are of equal size. To further improve the performance of the Gerard and Schucany test, a weighted test statistic is proposed and two weighting schemes are investigated. Seeking to further improve the performance of the proposed test, empirical Bayes estimates of the cluster-level success probabilities are utilized. These adaptations lead to 5 new tests, each of which are shown through simulation studies to be superior to the Gerard and Schucany (Comput Stat Data Anal 51:4622–4632, 2007) test. The proposed tests are further illustrated using data from a chemical repellency trial.
Starting Page	698
Ending Page	712
Page Count	15
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	21
Issue Number	4
e-ISSN	15372693
Language	English
Publisher	Springer US
Publisher Date	2016-07-22
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Binomial Clustered binary data Exact test Permutation test Power Sign test Statistics for Life Sciences, Medicine, Health Sciences Agriculture Monitoring/Environmental Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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