The goal of this project is to develop improved statistical methods for toxicology studies. Work has proceeded in two areas: (1) the analysis of multi-site tumor data from 2-year bioassays, and (2) risk assessment and testing in toxicology studies that measure multiple endpoints. Two-year bioassay studies are routinely conducted by the NTP to assess the tumorigenic potential of test agents. Current methods consider each tumor site separately. We have developed an approach for joint analysis of data from multiple tumor sites. This approach accounts for within-animal dependency, survival differences between groups and tumor lethality, while also allowing for the incorporation of historical control information. Multiple endpoints are often measured in reproductive and developmental toxicity studies. We have developed methods for assessing overall toxic effects in reproductive experiments when data include both the number of subunits per dam (litter size, number of implants) and multiple outcomes on each subunit (low birth weight, malformation). We have also developed a general framework for modeling of multivariate clustered data that enables joint estimation of effects on disparate outcomes such as the number of implantation sites per animal, the proportion of dead fetuses per dam, the proportion of malformed fetuses per dam, and birth weight. Methods are under development that are robust to distributional assumptions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Intramural Research (Z01)
Project #
1Z01ES040009-05
Application #
6672868
Study Section
(BB)
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2002
Total Cost
Indirect Cost
Name
U.S. National Inst of Environ Hlth Scis
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Wang, Lianming; Dunson, David B (2010) Semiparametric bayes multiple testing: applications to tumor data. Biometrics 66:493-501
Pennell, Michael L; Dunson, David B (2008) Nonparametric bayes testing of changes in a response distribution with an ordinal predictor. Biometrics 64:413-23
Pennell, Michael L; Dunson, David B (2007) Fitting semiparametric random effects models to large data sets. Biostatistics 8:821-34
Pennell, Michael L; Dunson, David B (2006) Bayesian semiparametric dynamic frailty models for multiple event time data. Biometrics 62:1044-52
Hans, Chris; Dunson, David B (2005) Bayesian inferences on umbrella orderings. Biometrics 61:1018-26
Dunson, David B; Herring, Amy H (2005) Bayesian latent variable models for mixed discrete outcomes. Biostatistics 6:11-25
Chen, Zhen; Dunson, David B (2004) Bayesian estimation of survival functions under stochastic precedence. Lifetime Data Anal 10:159-73
Dunson, David B; Chen, Zhen; Harry, Jean (2003) A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes. Biometrics 59:521-30
Dunson, David B; Watson, M; Taylor, Jack A (2003) Bayesian latent variable models for median regression on multiple outcomes. Biometrics 59:296-304
Dunson, David B; Dinse, Gregg E (2002) Bayesian models for multivariate current status data with informative censoring. Biometrics 58:79-88

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