title: Weighted multiplicity adjustments for animal carcinogenicity tests.
abstract: Sparse data is a difficulty in the analysis of animal carcinogenicity data: it is difficult to detect effects when the background tumor rates are low. The widely used "Haseman rule" and its variants provide more power to tests with low background rates, while maintaining a degree of control over the global false positive rate. In this article we explore the use of these rules, finding global error rates that are unacceptably high for many animal carcinogenicity studies. We provide alternative weighting methods that correct the deficiencies of the Haseman rule, and apply them to carcinogenicity data from a pharmaceutical company.
authors.affiliation: Department of Information Systems and Quantitative Sciences, Texas Tech University, Lubbock 79409, USA.
authors.forename: P H
authors.initials: PH
authors.lastname: Westfall
authors.forename: K A
authors.initials: KA
authors.lastname: Soper
mesh_terms: D000465:Algorithms; D000818:Animals; D015197:Carcinogenicity Tests / Q000706:statistics & numerical data*; D002273:Carcinogens / Q000633:toxicity; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D005260:Female; D008297:Male; D051379:Mice; D008815:Mice, Inbred Strains; D009374:Neoplasms, Experimental; D051381:Rats; D011916:Rats, Inbred F344
chemical_list: D002273:Carcinogens