A Bayesian hierarchical model for risk assessment of methylmercury
 Brent A. Coull,
 Maura Mezzetti,
 Louise M. Ryan
 … show all 3 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
This article uses a Bayesian hierarchical model to quantify the adverse health effects associated with inutero exposure to methylmercury. By allowing for studytostudyas well as outcometooutcome variability, the approach provides a useful metaanalytic tool for multioutcome, multistudy environmental risk assessments. The analysis presented here expands on the findings of a National Academy of Sciences (NAS) committee, charged with advising the United States Environmental Protection Agency (EPA) on an appropriate approach to conducting a risk assessment for methylmercury. The NAS committee, for which the senior author (Ryan) was a committee member, reviewed the findings from several conflicting studies and reported the results from a Bayesian hierarchical model that synthesized information across several studies and for several outcomes. Although the NAS committee did not suggest that the hierarchical model be used as the actual basis for a methylmercury risk assessment, the results from the model were used to justify and support the final recommendation that the risk analysis be based on data from a study conducted in the Faroe Islands, which had found a positive association between inutero exposure to methylmercury and impaired neurological development. We considera variety of statistical issues, but particularly sensitivity to model specification.
 Arends, L., Voko, Z., Stijnen, T. (2003) Combining Multiple Outcome Measures in a Metaanalysis: An Application. Statistics in Medicine 22: pp. 13351353 CrossRef
 Banga, S. J., Patil, G. P., Taillie, C. (2000) Sensitivity of Normal Theory Methods to Model Misspecification in the Calculation of Upper Confidence Limits on the Risk Function for Continuous Responses. Environmental and Ecological Statistics 7: pp. 177189 CrossRef
 Banga, S. J., Patil, G. P., Taillie, C. (2002) Direct Calculation of Likelihoodbased Benchmark Dose Levels for Quantitative Responses. Environmental and Ecological Statistics 9: pp. 295315 CrossRef
 Berkey, C., Hoaglin, D., Motseller, F., Colditz, G. (1995) A Random Effects Regression Model for Metaanalysis. Statistics in Medicine 14: pp. 395411 CrossRef
 Berkey, C. S., Hoaglin, D. C., AntczakBouckoms, A., Mosteller, F., Colditz, G. A. (1998) Metaanalysis of Multiple Outcomes by Regression with Random Effects. Statistics in Medicine 17: pp. 25372550 CrossRef
 Bosch, R. J., Wypij, D., Ryan, L. M. (1996) A Semiparametric Approach to Risk Assessment for Quantitative Outcomes. Risk Analysis 16: pp. 657665 CrossRef
 BudtzJorgensen, E., Keiding, N., Grandjean, P. (2001) Benchmark Dose Calculation from Epidemiological Data. Biometrics 57: pp. 698706 CrossRef
 Coull, B. A., Hobert, J. P., Ryan, L. M., Holmes, L. B. (2001) Crossed Random Effect Models for Multiple Outcomes in a Study of Teratogenesis. Journal of the American Statistical Association 96: pp. 11941204 CrossRef
 Crump, K. S. (1984) A New Method for Determining Allowable Daily Intakes. Fundamental and Applied Toxicology 4: pp. 854871 CrossRef
 Crump, K. S. (1995) Calculation of Benchmark Doses from Continuous Data. Risk Analysis 15: pp. 7989 CrossRef
 Crump, K. S., Kjellstrom, T., Shipp, A. M., Silvers, A., Stewart, A. (1998) Influence of Prenatal Mercury Exposure Upon Scholastic and Psychological Test Performance: Benchmark Analysis of a New Zealand Cohort. Risk Analysis 18: pp. 701713 CrossRef
 Crump, K. S., Landingham, C., Shamlaye, C., Cox, C., Davidson, P. W., Myers, G. J., Clarkson, T. W. (2000) Benchmark Concentrations for Methylmercury Obtained from the Seychelles Child Development Study. Environmental Health Perspectives 108: pp. 257263 CrossRef
 Daniels, M., Kass, R. (1998) A Note on Firststage Approximation in Twostage Hierarchical Models. Sankltya B 60: pp. 1930
 Davidson, P. W., Myers, G. J., Cox, C., Axtell, C., Shamlaye, C., SloaneReeves, J., Cernichiari, E., Needham, L., Choi, A., Wang, Y., Berlin, M., Clarkson, T. W. (1998) Effects of Prenatal and Postnatal Methylmercury Exposure from Fish Consumption on Neurodevelopment: Outcomes at 66 Months of Age in the Seychelles Child Development Study. Journal of the American Medical Association 280: pp. 701707 CrossRef
 DerSimonian, R., Laird, N. (1986) Metaanalysis in Clinical Trials. Controlled Clinical Trials 7: pp. 177188 CrossRef
 Dominici, F., Samet, J. M., Zeger, S. L. (2000) Combining Evidence on Air Pollution and Daily Mortality from the 20 Largest US Cities: A Hierarchical Modelling Strategy. Journal of the Royal Statistical Society, Series A 163: pp. 263284
 Gaylor, D. W., Slikker, W. Risk Assessment for Neurotoxicants. In: Tilson, H., Mitchell, C. eds. (1992) Neurotoxicology. Raven Press, New York, pp. 341353
 Gelfand, A. E., Sahu, S. K., Carlin, B. P. (1995) Efficient Parameterisations for Normal Linear Mixed Models. Biometrika 82: pp. 479488 CrossRef
 Gilbert, S. G., Grantwebster, K. S. (1995) Neurobehavioral Effects of Developmental Methylmercury Exposure. Environmental Health Perspectives 103: pp. 135142 CrossRef
 Grandjean, P., Weihe, P., White, R. F., Debes, F., Araki, S., Yokoyama, K., Murata, K., Sorensen, N., Dahl, R., Jorgensen, P. J. (1997) Cognitive Deficit in 7yearold Children with Prenatal Exposure to Methy Imercury. Neurotoxicological Teratology 19: pp. 417428 CrossRef
 Morgan, B. J. T. (1992) Analysis of Quantal Response Data. Chapman & Hall, London
 Myers, G. J., Davidson, P. W., Cox, C., Shamlaye, C. F., Tanner, M. A., Choisy, O., SloaneReeves, J., Marsh, D. O., Cernichiari, E., Choi, A., Berlin, M., Clarkson, T. W. (1995) Neurodevelopmental Outcomes of Seychellois Children Sixtysix Months After in Utero Exposure to Methy Imercury from a Maternal Fish Diet: Pilot Study. Neurotoxicology 16: pp. 639652
 Natarajan, R., Kass, R. E. (2000) Reference Bayesian Methods for Generalized Lineralized Linear Mixed Models. Journal of the American Statistical Association 95: pp. 227237 CrossRef
 Combining Information: Statistical Issues and Opportunities for Research. National Academy Press, Washington, DC
 Health Effects of Methylmercury. National Academy Press, Washington, DC
 Simmons, S. J., Piegorsch, W. W., Nitcheva, D., Zeiger, E. (2003) Combining Environmental Information via Hierarchical Modeling: An Example Using Mutagenic Potencies. Environmetrics 14: pp. 159168 CrossRef
 Spiegelhalter, D., Thomas, A., Best, N. (2000) Win BUGS Version 1.3 User’s Manual. MRC Biostatistics Unit, Institute of Public Health, Cambridge
 Spiegelhalter, D. J., Best, N. G., Carlin, B. P., Linde, A. (2002) Bayesian Measures of Model Complexity and Fit. Journal of the Royal Statistical Society, Series B 64: pp. 583640 CrossRef
 Houwelingen, H. C., Arends, L., Stijnen, T. (1998) Advanced Methods in Metaanalysis: Multivariate Approach and Metaregression. Statistics in Medicine 21: pp. 589624 CrossRef
 Watanabe, C., Satoh, H. (1996) Evolution of Our Understanding of Methylmercury as a Health Threat. Environmental Health Perspectives 104: pp. 367379 CrossRef
 West, R. W., Kodell, R. L. (1993) Statistical Methods of Risk Assessment for Continuous Variables. Communications in Statistics—Theory and Methods 22: pp. 33633376 CrossRef
 Title
 A Bayesian hierarchical model for risk assessment of methylmercury
 Journal

Journal of Agricultural, Biological, and Environmental Statistics
Volume 8, Issue 3 , pp 253270
 Cover Date
 20030901
 DOI
 10.1198/1085711032291
 Print ISSN
 10857117
 Online ISSN
 15372693
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Benchmark dose
 Doseresponse
 Gibbs sampling
 Linear mixed model
 Metaanalysis
 Multiple outcomes
 Win BUGS
 Authors

 Brent A. Coull ^{(1)}
 Maura Mezzetti ^{(2)}
 Louise M. Ryan ^{(3)}
 Author Affiliations

 1. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, 02115, Boston, MA
 2. Istitutodi Metodi Quantitativi, Università Bocconi, Milano, Italy
 3. Department of Biostatistical Science, Harvard School of Public Health and Data Farber Cancer Institute, 02115, Boston, MA