Grantee Research Project Results
Final Report: Statistical Methods for Non-Cancer Risk Assessment
EPA Grant Number: R824757Title: Statistical Methods for Non-Cancer Risk Assessment
Investigators: Ryan, Louise
Institution: Harvard University
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 1995 through September 30, 1998
Project Amount: $410,892
RFA: Human Health Risk Assessment (1995) RFA Text | Recipients Lists
Research Category: Human Health
Objective:
The overall objective of this research was to address a series of statistical problems in noncancer risk assessment, with particular emphasis on reproduction and development. Proposed topics of study ranged from optimal experimental design for the Segment II developmental toxicology experiment to Bayesian methods for Benchmark Dose calculation. Specific research objectives included: (1) optimal experimental design for the conduct of dose-response studies and calculation of Benchmark Doses; (2) optimal sequential design procedures to identify LD_50s, ED_50s, and other quantiles for dose-rate studies (i.e., studies designed to assess the effects of duration as well as the concentration of exposure on adverse health effects; (3) incorporation of biomarkers and biologically-based dose measures into dose-response models and subsequent Benchmark Dose calculations; (4) non-parametric calculation of Benchmark Doses based on continuous outcomes, such as fetal weight, serum levels, etc.; (5) incorporation of historical control information into the analysis of multiple outcome data from developmental toxicity studies; and (6) use of meta-analysis techniques to estimate Benchmark Doses for developmental toxicology.
Summary/Accomplishments (Outputs/Outcomes):
Progress on this project was excellent. As listed below, the students and faculty supported by the project have produced many papers published in good statistical and applied journals, addressing the research questions discussed above. The grant also has been invaluable to the Department in terms of allowing us to maintain a strong and cohesive research group whose members are tackling a variety of problems related to dose response modeling in reproductive and developmental toxicology.
The work supported by this grant is very useful. There is intense awareness of the need for improved and more sophisticated methods of risk assessment for noncancer endpoints in general, and for reproduction and development in particular. Although the ideas of dose-response modeling are well established in the carcinogenicity setting, quantitative risk assessment for developmental and reproductive toxicity was still a relatively new field of study in the mid-1990s.
Journal Articles on this Report : 15 Displayed | Download in RIS Format
| Other project views: | All 15 publications | 15 publications in selected types | All 15 journal articles |
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Baird SJS, Catalano PJ, Ryan LM, Evans JS. Evaluation of effect profiles: functional observational battery outcomes. Toxicological Sciences 1997;40(1):37-51. |
R824757 (Final) |
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Betensky RA, Lindsey JC, Ryan LM, Wand MP. Local EM estimation of the hazard function for interval-censored data. Biometrics 1999;55(1):238-245. |
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Bosch RJ, Wypij D, Ryan LM. A semiparametric approach to risk assessment for quantitative outcomes. Risk Analysis 1996;16(5):657-665. |
R824757 (Final) |
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Catalano PJ. Bivariate modelling of clustered continuous and ordered categorical outcomes. Statistics in Medicine 1997;16(8):883-900. |
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Catalano PJ, McDaniel KL, Moser VC. The IPCS collaborative study on neurobehavioral screening methods: VI. Agreement and reliability of the data. Neurotoxicology 1997;18(4):1057-1064. |
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Geys H, Molenberghs G, Ryan LM. Pseudolikelihood modeling of multivariate outcomes in developmental toxicology. Journal of the American Statistical Association 1999;94(447):734-745. |
R824757 (Final) |
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Ibrahim JG, Ryan LM. Use of historical controls in time-adjusted trend tests for carcinogenicity. Biometrics 1996;52(4):1478-1485. |
R824757 (Final) |
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Ibrahim JG, Ryan LM, Chen MH. Using historical controls to adjust for covariates in trend tests for binary data. Journal of the American Statistical Association 1998;93(444):1282-1293 |
R824757 (Final) |
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Ibrahim JG, Chen M-H. Power prior distributions for regression models. Statistical Science 2000;15(1):46-60. |
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Regan MM, Catalano PJ. Likelihood models for clustered binary and continuous outcomes: application to developmental toxicology. Biometrics 1999;55(3):760-768. |
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Regan MM, Catalano PJ. Bivariate dose-response modeling and risk estimation in developmental toxicology. Journal of Agricultural, Biological, and Environmental Statistics 1999;4(3):217-237. |
R824757 (Final) R824754 (Final) |
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Sammel M, Lin X, Ryan L. Multivariate linear mixed models for multiple outcomes. Statistics in Medicine 1999;18(17-18):2479-2492. |
R824757 (Final) |
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Weller EA, Catalano PJ, Williams PL. Implications of developmental toxicity study design for quantitative risk assessment. Risk Analysis 1995;15(5):567-574. |
R824757 (Final) |
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Weller EA, Ryan LM. Testing for trend with count data. Biometrics 1998;54(2):762-773. |
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Williams P, Ryan L. Design of multiple binary outcome studies with intentionally missing data. Biometrics 1996;52(4):1498-1514. |
R824757 (Final) |
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Supplemental Keywords:
risk assessment, human health, noncancer endpoints, biostatistics, modeling, dose-response modeling., RFA, Health, Economic, Social, & Behavioral Science Research Program, Risk Assessments, Environmental Statistics, benchmark dose method, data synthesis, hierarchical Bayesian space-time models, infertility risk, risk assessment, biomarkers, Bayesian space-time model, toxicology, non-cancer risk assessment, computer models, environmental risks, dose-response, developmental toxicology, environmental mutagens, human exposure, statistical models, toxic environmental contaminants, Ethylene oxide, data analysis, sampling, reproductive health, data models, innovative statistical models, meta-analysisProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.