Model uncertainty and bayesian model averaged benchmark dose estimation for continuous data

Risk Analysis

Volumn 34, Issue 1, 2014, Pages 101-120

  Shao, Kan ^a   Gift, Jeffrey S ^a  

^a U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

Bayesian model averaging; Benchmark dose; Continuous data; Model uncertainty

Indexed keywords

BAYESIAN MODEL AVERAGING; BENCHMARK DOSE; CONTINUOUS DATA; DOSE-RESPONSE MODELS; ESTIMATION APPROACHES; MARKOV CHAIN MONTE-CARLO; MODEL UNCERTAINTIES; RISK ASSESSMENT TOOL;

BAYESIAN NETWORKS; COMPUTER SIMULATION; HEALTH RISKS; MAXIMUM LIKELIHOOD ESTIMATION; RISK ASSESSMENT; RISK PERCEPTION; UNCERTAINTY ANALYSIS;

ESTIMATION;

BENCHMARKING; BOOTSTRAPPING; DOSE-RESPONSE RELATIONSHIP; HEALTH RISK; MARKOV CHAIN; MAXIMUM LIKELIHOOD ANALYSIS; MONTE CARLO ANALYSIS; NUMERICAL MODEL; RISK ASSESSMENT; UNCERTAINTY ANALYSIS;

ANIMAL; BAYES THEOREM; BENCHMARKING; BODY WEIGHT; COMPUTER SIMULATION; DOSE RESPONSE; DRUG EFFECTS; HUMAN; MARKOV CHAIN; MONTE CARLO METHOD; ORGAN SIZE; PROCEDURES; RISK ASSESSMENT; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TOXICOLOGY; UNCERTAINTY;

ANIMALS; BAYES THEOREM; BENCHMARKING; BODY WEIGHT; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DOSE-RESPONSE RELATIONSHIP, DRUG; HUMANS; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; ORGAN SIZE; RISK ASSESSMENT; TOXICOLOGICAL PHENOMENA; UNCERTAINTY;

EID: 84892480080     PISSN: 02724332     EISSN: 15396924     Source Type: Journal    
DOI: 10.1111/risa.12078     Document Type: Article

References (46)

1. Crump KS. A new method for determining allowable daily intakes. Fundamental and Applied Toxicology, 1984; 4(5):854-871.
2. U.S. EPA. Benchmark dose technical guidance document. EPA/100/R-12/001. Washington, DC: U.S. Environmental Protection Agency, Risk Assessment Forum, 2012.
3. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974; 19:716-723.
4. Kang SH, Kodell RL, Chen JJ. Incorporating model uncertainties along with data uncertainties in microbial risk assessment. Regulatory Toxicology and Pharmacology, 2000; 31(1):68-72.
5. Moon H, Kim HJ, Chen JJ, Kodell RL. Model averaging using the Kullback information criterion in estimating effective doses for microbial infection and illness. Risk Analysis, 2005; 25(5):1147-1159.
6. Morales KH, Ibrahim JG, Chen CJ, Ryan LM. Bayesian model averaging with applications to benchmark dose estimation for arsenic in drinking water. Journal of the American Statistical Association, 2006; 101(473):9-17.
7. Wheeler MW, Bailer AJ. Properties of model-averaged BMDLs: A study of model averaging in dichotomous response risk estimation. Risk Analysis, 2007; 27(3):659-670.
8. Shao K, Small MJ. Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study. Risk Analysis, 2011; 31(10):1561-1575.
9. Shao K. A comparison of three methods for integrating historical information for Bayesian model averaged benchmark dose estimation. Environmental Toxicology and Pharmacology, 2012; 34(2):288-296.
10. Gaylor DW, Slikker W, Jr. Risk assessment for neurotoxic effects. Neurotoxicology, 1990; 11(2):211-218.
11. Crump KS. Calculation of benchmark doses from continuous data. Risk Analysis, 1995; 15(1):79-89.
12. West RW, Kodell RL. A comparison of methods of benchmark-dose estimation for continuous response data. Risk Analysis, 1999; 19(3):453-459.
13. Crump KS. Critical issues in benchmark calculations from continuous data. Critical Reviews in Toxicology, 2002; 32(3):133-153.
14. Crump KS, Howe RB. A review of methods for calculating statistical confidence limits in low dose extrapolation. Pp. 187-203 in Clayson DB, Krewski D, Munro I (eds). Toxicological Risk Assessment, Vol. I. Boca Raton, FL: CRC Press, 1985.
15. Kodell RL, West RW. Upper confidence limits on excess risk for quantitative responses. Risk Analysis, 1993; 13(2):177-182.
16. West RW, Kodell RL. Statistical methods of risk assessment for continuous variables. Communications in Statistics: Theory and Methods, 1993; 22(12):3363-3376.
17. Budtz-Jørgensen E, Keiding N, Grandjean P. Benchmark dose calculation from epidemiological data. Biometrics, 2001; 57:698-706.
18. Moerbeek M, Piersma AH, Slob W. A comparison of three methods for calculating confidence intervals for the benchmark dose. Risk Analysis, 2004; 24(1):31-40.
19. Madigan D, Raftery AE. Model selection and accounting for model uncertainty in graphical models using Occams window. Journal of the American Statistical Association, 1994; 89(428):1535-1546.
20. Kass RE, Raftery AE. Bayes factors. Journal of the American Statistical Association, 1995; 90(430):773-795.
21. Raftery AE. Approximate Bayes factors accounting for model uncertainty in generalised linear models. Biometrika, 1996; l83(2):251-266.
22. Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: A tutorial. Statistical Science, 1999; 14(4):382-417.
23. Montgomery JM, Nyhan B. Bayesian model averaging: Theoretical developments and practical applications. Political Analysis, 2010; 18(2):245-270.
24. Kang SH, Kodell RL, Chen JJ. Incorporating model uncertainties along with data uncertainties in microbial risk assessment. Regulatory Toxicology and Pharmacology, 2000; 31(1):68-72.
25. Bailer AJ, Noble RB, Wheeler MW. Model uncertainty and risk estimation for experimental studies of quantal responses. Risk Analysis, 2005; 25(2):291-299.
26. Shao K, Small MJ. Statistical evaluation of toxicological experimental design for bayesian model averaged benchmark dose estimation with dichotomous data. Human and Ecological Risk Assessment, 2012; 18(5):1096-1119.
27. U.S. EPA. Benchmark Dose Software (BMDS). Research Triangle Park, NC: National Center for Environmental Assessment, 2011.
28. Slob W, Pieters MN. A probabilistic approach for deriving acceptable human intake limits and human health risks from toxicological studies: General framework. Risk Analysis, 1998; 18(6):787-798.
29. Sand SJ, von Rosen D, Filipsson AF. Benchmark calculations in risk assessment using continuous dose-response information: The influence of variance and the determination of a cut-off value. Risk Analysis, 2003; 23(5):1059-1068.
30. Slob W. dose-response modeling of continuous endpoints. Toxicological Science, 2002; 66(2):298-312.
31. Raftery AE. Bayesian model selection in social research. Sociological Methodology, 1995; 25:111-163.
32. Wasserman L. Bayesian model selection and model averaging. Journal of Mathematical Psychology, 2000; 44(1):92-107.
33. Kass RE, Wasserman L. A reference Bayesian test for nested hypotheses and its relationship to the Schwartz criterion. Journal of the American Statistical Association, 1995; 90:928-934.
34. Schwarz G. Estimating the dimension of a model. Annals of Statistics, 1978; 6(2):461-464.
35. Buckland ST, Burnham KP, Augustin NH. Model selection: An integral part of inference. Biometrics, 1997; 53(2):603-618.
36. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. New York: Springer, 2002.
37. Kullback S. Information Theory and Statistics. New York: Dover Publications, 1968.
38. Efron B. The Jackknife, the Bootstrap and Other Resampling Plans. Society for Industrial and Applied Mathematics. England: J.W. Arrowsmith Ltd, 1982.
39. Newton MA, Raftery AE. Approximate Bayesian inference with the weighted likelihood bootstrap. Journal of the Royal Statistical Society: Series B, 1994; 56(1):3-48.
40. Carlin BP, Chib S. Bayesian model choice via Markov-chain Monte-Carlo methods. Journal of the Royal Statistical Society: Series B, 1995; 57(3):473-484.
41. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis, 2nd ed. Boca Raton, FL: Chapman and Hall/CRC Press, 2003.
42. McCauley PT, Robinson M, Daniel FB, Olson GR. The effects of subacute and subchronic oral exposure to CIS-1, 2-dichloroethylene in sprague-dawley rats. Drug and Chemical Toxicology, 1995; 18(2-3):171-184.
43. NTP (National Toxicology Program). NTP Technical Report on the Toxicity Studies of Butanal Oxime Administered in Drinking Water and by Gavage to F344/N Rats and B6C3F1 Mice. NTP TOX 69. NC: Research Triangle Park, 2004.
44. Gelman A, Rubin DB. Inference from interative simulation using multiple sequences (with discussion). Statistical Science, 1992; 7:457-511.
45. Breiman L. Bagging predictors. Machine Learning, 1996; 24(2):123-140.
46. Davis JA, Gift JS, Zhao QJ. Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1. Toxicology and Applied Pharmacology, 2011; 254(2):181-191.