Multilevel Empirical Bayes Modeling for Improved Estimation of Toxicant Formulations to Suppress Parasitic Sea Lamprey in the Upper Great Lakes
Estimation of extreme quantal‐response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to...
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| Published in | Biometrics Vol. 67; no. 3; pp. 1153 - 1162 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.09.2011
Wiley-Blackwell Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0006-341X 1541-0420 1541-0420 |
| DOI | 10.1111/j.1541-0420.2011.01566.x |
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| Abstract | Estimation of extreme quantal‐response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out‐of‐sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. |
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| AbstractList | Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants is critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. Summary Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data.Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. |
| Author | Gutreuter, Steve Boogaard, Michael A. Carlin, Bradley P. Hatfield, Laura A. |
| AuthorAffiliation | 2 U.S. Geological Survey, Upper Midwest Environmental Sciences Center, La Crosse, WI 54603 1 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21361894$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1139/f68-063 10.1897/06-630R.1 10.1897/02-501 10.1023/A:1008929526011 10.2307/1390675 10.18637/jss.v012.i03 10.1139/f07-082 10.1577/M06-178.1 10.1146/annurev.pa.14.040174.000403 10.1214/aos/1176325625 10.1111/1467-9868.00353 10.1214/ss/1177011136 10.1002/sim.3680 10.1139/f80-222 10.2307/2531147 10.1016/S0380-1330(03)70512-7 10.1016/j.ecolecon.2004.10.002 |
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| References | Brooks, S. and Gelman, A. (1997). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434-455. Smith, B. and Tibbles, J. (1980). Sea lamprey (Petromyzon marinus) in lakes Huron, Michigan and Superior: History of invasion and control, 1936-78. Canadian Journal of Fisheries and Aquatic Sciences 37, 1780-1801. Stiratelli, R., Laird, N., and Ware, J. (1984). Random-effects models for serial observations with binary response. Biometrics 40, 961-971. Smith, S. (1968). Species succession and fishery exploitation in the Great Lakes. Journal of the Fisheries Research Board of Canada 25, 667-693. Yang, R. and Berger, J. (1994). Estimation of a covariance matrix using the reference prior. Annals of Statistics 22, 1195-1211. Scholefield, R., Slaght, K., and Stephens, B. (2008). Seasonal variation in sensitivity of larval sea lampreys to the lampricide 3-trifluoromethyl-4-nitrophenol. North American Journal of Fisheries Management 28, 1609-1617. Bills, T., Boogaard, M., Johnson, D., Brege, D., Scholefield, R., Westman, R., and Stephens, B. (2003). Development of a pH/alkalinity treatment model for applications of the lampricide TFM to streams tributary to the Great Lakes. Journal of Great Lakes Research 29, 510-520. Spiegelhalter, D., Best, N., Carlin, B., and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583-639. Gelderen, E. V., Ryan, A., Tomasso, J., and Klaine, S. (2003). Influence of dissolved organic matter source on silver toxicity to Pimephales pomelas. Environmental Toxicology and Chemistry 22, 2746-2751. Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7, 457-472. Hunn, J. and Allen, J. (1974). Movement of drugs across the gills of fishes. Annual Review of Pharmacology 14, 45-57. Davidian, M. and Giltinan, D. (1995). Nonlinear Models of Repeated Measurement Data. Boca Raton , Florida : Chapman & Hall/CRC. Wilkie, M., Holmes, J., and Youson, J. (2007). The lampricide 3-trifluoromethyl-4-nitrophenol (TFM) interferes with intermediary metabolism and glucose homeostasis, but not with ion balance, in larval sea lamprey (Petromyzon marinus). Canadian Journal of Fisheries and Aquatic Sciences 64, 1174-1182. Lunn, D., Spiegelhalter, D., Thomas, A., and Best, N. (2009). The BUGS project: Evolution, critique and future directions. Statistics in Medicine 28, 3049-3067. Pimentel, D., Zuniga, R., and Morrison, D. (2005). Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 52, 273-288. Sturtz, S., Ligges, U., and Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software 12, 1-16. Lunn, D., Thomas, A., Best, N., and Spiegelhalter, D. (2000). WinBUGS---a Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10, 325-337. Gutreuter, S. and Boogaard, M. (2007). Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of variance. Environmental Toxicology and Chemistry 26, 1978-1986. 1968; 25 1992; 7 1984; 40 1974; 14 1980; 37 2002; 64 2000; 10 2008; 28 2005; 52 1994; 22 1973 1995 2003; 29 2000; Volume 11.05 2007; 64 2005; 12 1997; 7 2003; 22 2007; 26 2009; 28 Gutreuter (10.1111/j.1541-0420.2011.01566.x-BIB8|cit8) 2007; 26 Davidian (10.1111/j.1541-0420.2011.01566.x-BIB5|cit5) 1995 Hunn (10.1111/j.1541-0420.2011.01566.x-BIB9|cit9) 1974; 14 Scholefield (10.1111/j.1541-0420.2011.01566.x-BIB13|cit13) 2008; 28 Gelderen (10.1111/j.1541-0420.2011.01566.x-BIB6|cit6) 2003; 22 Sturtz (10.1111/j.1541-0420.2011.01566.x-BIB18|cit18) 2005; 12 Spiegelhalter (10.1111/j.1541-0420.2011.01566.x-BIB16|cit16) 2002; 64 Stiratelli (10.1111/j.1541-0420.2011.01566.x-BIB17|cit17) 1984; 40 Yang (10.1111/j.1541-0420.2011.01566.x-BIB20|cit20) 1994; 22 Pimentel (10.1111/j.1541-0420.2011.01566.x-BIB12|cit12) 2005; 52 American Society for Testing and Materials (10.1111/j.1541-0420.2011.01566.x-BIB2|cit2) 2000; Volume 11.05 Gelman (10.1111/j.1541-0420.2011.01566.x-BIB7|cit7) 1992; 7 Smith (10.1111/j.1541-0420.2011.01566.x-BIB14|cit15) 1968; 25 Akaike (10.1111/j.1541-0420.2011.01566.x-BIB1|cit1) 1973 Lunn (10.1111/j.1541-0420.2011.01566.x-BIB11|cit11) 2009; 28 Wilkie (10.1111/j.1541-0420.2011.01566.x-BIB19|cit19) 2007; 64 Brooks (10.1111/j.1541-0420.2011.01566.x-BIB4|cit4) 1997; 7 Bills (10.1111/j.1541-0420.2011.01566.x-BIB3|cit3) 2003; 29 Lunn (10.1111/j.1541-0420.2011.01566.x-BIB10|cit10) 2000; 10 Smith (10.1111/j.1541-0420.2011.01566.x-BIB15|cit14) 1980; 37 |
| References_xml | – reference: Davidian, M. and Giltinan, D. (1995). Nonlinear Models of Repeated Measurement Data. Boca Raton , Florida : Chapman & Hall/CRC. – reference: Spiegelhalter, D., Best, N., Carlin, B., and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583-639. – reference: Bills, T., Boogaard, M., Johnson, D., Brege, D., Scholefield, R., Westman, R., and Stephens, B. (2003). Development of a pH/alkalinity treatment model for applications of the lampricide TFM to streams tributary to the Great Lakes. Journal of Great Lakes Research 29, 510-520. – reference: Smith, S. (1968). Species succession and fishery exploitation in the Great Lakes. Journal of the Fisheries Research Board of Canada 25, 667-693. – reference: Brooks, S. and Gelman, A. (1997). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434-455. – reference: Smith, B. and Tibbles, J. (1980). Sea lamprey (Petromyzon marinus) in lakes Huron, Michigan and Superior: History of invasion and control, 1936-78. Canadian Journal of Fisheries and Aquatic Sciences 37, 1780-1801. – reference: Wilkie, M., Holmes, J., and Youson, J. (2007). The lampricide 3-trifluoromethyl-4-nitrophenol (TFM) interferes with intermediary metabolism and glucose homeostasis, but not with ion balance, in larval sea lamprey (Petromyzon marinus). Canadian Journal of Fisheries and Aquatic Sciences 64, 1174-1182. – reference: Lunn, D., Thomas, A., Best, N., and Spiegelhalter, D. (2000). WinBUGS---a Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10, 325-337. – reference: Hunn, J. and Allen, J. (1974). Movement of drugs across the gills of fishes. Annual Review of Pharmacology 14, 45-57. – reference: Gelderen, E. V., Ryan, A., Tomasso, J., and Klaine, S. (2003). Influence of dissolved organic matter source on silver toxicity to Pimephales pomelas. Environmental Toxicology and Chemistry 22, 2746-2751. – reference: Pimentel, D., Zuniga, R., and Morrison, D. (2005). Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 52, 273-288. – reference: Scholefield, R., Slaght, K., and Stephens, B. (2008). Seasonal variation in sensitivity of larval sea lampreys to the lampricide 3-trifluoromethyl-4-nitrophenol. North American Journal of Fisheries Management 28, 1609-1617. – reference: Lunn, D., Spiegelhalter, D., Thomas, A., and Best, N. (2009). The BUGS project: Evolution, critique and future directions. Statistics in Medicine 28, 3049-3067. – reference: Sturtz, S., Ligges, U., and Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software 12, 1-16. – reference: Stiratelli, R., Laird, N., and Ware, J. (1984). 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| Snippet | Estimation of extreme quantal‐response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the... Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the... Summary Estimation of extreme quantal‐response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in... Summary Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in... |
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| SubjectTerms | Alkalinity analysis Animals Bayes Theorem Bayesian analysis Bayesian networks BIOMETRIC PRACTICE Biometrics Biometry Biometry - methods Covariance matrices Estimating techniques Great Lakes Hazardous Substances Hazardous Substances - analysis Hazardous Substances - pharmacology hydrochemistry Introduced Species Introduced Species - statistics & numerical data invasive species Lakes Larvae Lethal concentration/dose Markov chain Monte Carlo (MCMC) methods Modeling Models, Statistical Multilevel models Nonlinear model North America Parametric models Parasites pest control Pesticides Pesticides - analysis Pesticides - toxicity Petromyzon Petromyzon marinus Petromyzontidae pharmacology prediction Quantal-response bioassay seasonal variation Seasons Simulations statistics statistics & numerical data toxic substances toxicity Toxicity Tests |
| Title | Multilevel Empirical Bayes Modeling for Improved Estimation of Toxicant Formulations to Suppress Parasitic Sea Lamprey in the Upper Great Lakes |
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