Multilevel Empirical Bayes Modeling for Improved Estimation of Toxicant Formulations to Suppress Parasitic Sea Lamprey in the Upper Great Lakes
Article first published online: 1 MAR 2011
© 2011, The International Biometric Society
Volume 67, Issue 3, pages 1153–1162, September 2011
How to Cite
Hatfield, L. A., Gutreuter, S., Boogaard, M. A. and Carlin, B. P. (2011), Multilevel Empirical Bayes Modeling for Improved Estimation of Toxicant Formulations to Suppress Parasitic Sea Lamprey in the Upper Great Lakes. Biometrics, 67: 1153–1162. doi: 10.1111/j.1541-0420.2011.01566.x
- Issue published online: 14 SEP 2011
- Article first published online: 1 MAR 2011
- Received December 2009. Revised October 2010. Accepted October 2010.
- Lethal concentration/dose;
- Markov chain Monte Carlo (MCMC);
- Nonlinear model;
- Quantal-response bioassay
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.