Using Priors to Improve Multiple Animal Carcinogenicity Tests

• Published in: Journal of the American Statistical Association Vol. 96; no. 455; pp. 827 - 834
• Main Authors: Westfall, Peter H, Soper, Keith A
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.09.2001; American Statistical Association; Taylor & Francis Ltd
• Subjects: Animals; Applications; Applications and Case Studies; Approximation; Bayesian method; Biology, psychology, social sciences; Biometrics; Cancer; Carcinogenicity; Carcinogenicity tests; Carcinogens; Computer software; Critical values; Error rates; Exact sciences and technology; Extrabinomial variation; Forecasts; Frequentism; Mathematics; Medical sciences; Multiple testing; P values; Predictive power; Probability and statistics; Sciences and techniques of general use; Statistical methods; Statistics; Tumorigenicity test; Tumors; Biometrics; Bayes estimation; Conditional distribution; Critical value; Error estimation; Prior distribution; Error rate; Multiple test; Statistical method; Medical science; Carcinogenicity testing; Exact test; Trend analysis; Distribution function; Tumor; Age dependence; Test power; Sampling; Pharmaceutical industry; Animal population
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/016214501753208852

This article reviews and summarizes methods for controlling false positives in animal carcinogenicity studies and promotes an alternative that incorporates historical control information via Bayesian methods. The Bayesian paradigm is used as a procedure generator; however, frequentist multisample, age-stratified exact trend tests are used in the ultimate analysis. Critical values for the exact tests are chosen to maximize total expected power, conditional on tumor totals, by using prior distributions. To control the risk of a false-positive finding for one or more tumor types, the sum of the individual critical levels is constrained to be less than a nominal familywise error rate, such as .05. The resulting tests give more power to tumor types with higher-than-expected tumor totals. We use historical control data from animal carcinogenicity studies obtained from a large pharmaceutical company to train and evaluate the tests. There is greatly enhanced power of the proposed method, with concurrent error rate control, because the targeting procedure gives higher power to affected sites, and the procedure tends to produce critical values that are as small as possible overall (implying higher power), subject to the overall risk level constraint. Randomly sampling from real historical animal populations, we compare operating characteristics of various methods proposed in the literature and requested by regulatory agencies. Commonly used methods can have greatly inflated false-positive rates, particularly with larger studies. In some cases, we find greater power for the proposed method, even compared to methods that do not control false positives.