Bayesian methods for a three-state model for rodent carcinogenicity studies.
The objective of a chronic rodent bioassay is to assess the impact of a chemical compound on the development of tumors. However, most tumor types are not observable prior to necropsy, making direct estimation of the tumor incidence rate problematic. In such cases, estimation can proceed only if the study incorporates multiple interim sacrifices or we make use of simplified parametric or nonparametric models. In addition, it is widely accepted that other factors, such as weight, can be related to both dose level and tumor onset, confounding the association of interest. However, there is not typically enough information in the current study to assess such effects. The addition of historical data can help alleviate this problem. In this article, we propose a novel Bayesian semiparametric model for the analysis of data from rodent carcinogenicity studies. We develop informative prior distributions for covariate effects through the use of historical control data and outline a Gibbs sampling scheme. We implement the model by analyzing data from a National Toxicology Program chronic rodent bioassay.
French
Jonathan L
JL
Biostatistics, Pfizer Global Research and Development, 50 Pequot Avenue, New London, Connecticut 06320, USA. Jonathan_L_French@groton.pfizer.com
Ibrahim
Joseph G
JG
D000818:Animals; D001499:Bayes Theorem; D001835:Body Weight; D015197:Carcinogenicity Tests / Q000379:methods*; D003198:Computer Simulation; D004003:Dichlorophen / Q000031:analogs & derivatives* / Q000378:metabolism / Q000633:toxicity; D004202:Disinfectants / Q000378:metabolism / Q000633:toxicity; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000139:chemically induced; D051379:Mice; D008809:Mice, Inbred C3H; D008810:Mice, Inbred C57BL; D008954:Models, Biological; D015233:Models, Statistical; D051381:Rats; D011916:Rats, Inbred F344
D004202:Disinfectants; C004660:clorophene; D004003:Dichlorophen