Abstract
For any definition of additivity, evaluating whether an organism’s response to a mixture is additive depends on the dose-response relationships for each of the mixture’s component chemicals. Consequently, the statistical analysis of dose-response relationships is fundamental to mixture toxicology – as well as to other areas of toxicology. This chapter offers a broad overview of dose-response modeling and an introduction to some statistical issues that arise in the use of dose-response models – with an eye to evaluating additivity. It does not, however, attempt to be a handbook or guide to the use of any specific models; instead, it tries to make readers aware of issues that need attention to achieve efficient and valid inference. The chapter mentions features of study design and describes how they can influence both aspects of model fitting and the quality of results. It considers the choice of functional form used to describe how the mean response changes as dose increases as well as the evaluation of how well the chosen form fits the data at hand. The chapter also points out that proper modeling of the variability inherent in the structure of the data is crucial to efficient statistical inference. Finally, because many dose-response models require iterative numerical methods, it offers a few pointers to help overcome problems when these methods fail to converge. Dose-response modeling is an essential tool in mixture toxicology but one that demands careful application to achieve the best results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agresti, A. 2013. Categorical data analysis. 3rd ed. Hoboken: Wiley.
Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 (6): 716–723.
Altenburger, R., T. Backhaus, W. Boedeker, M. Faust, M. Scholze, and L.H. Grimme. 2000. Predictability of the toxicity of multiple chemical mixtures to vibrio fischeri: Mixtures composed of similarly acting chemicals. Environmental Toxicology and Chemistry 19 (9): 2341–2347.
Bates, D.M., and D.G. Watts. 1988. Nonlinear regression analysis and its applications. New York: Wiley.
Berenbaum, M.C. 1985. The expected effect of a combination of agents: The general solution. Journal of Theoretical Biology 114: 413–431.
Bernstein, L., J. Kaldor, J. McCann, and M.C. Pike. 1982. An empirical approach to the statistical analysis of mutagenesis data from the Salmonella test. Mutation Research 97: 267–281.
Bickel, P.J., and K.A. Doksum. 1977. Mathematical statistics: Basic ideas and selected topics. San Francisco: Holden-Day.
Box, G.E.P., and D.R. Cox. 1964. An analysis of transformations. Journal of the Royal Statistical Society, Series B 26 (2): 211–252.
Box, G.E.P., and G.C. Tiao. 1992. Bayesian inference in statistical analysis. New York: Wiley.
Breslow, N.E. 1984. Extra-poisson variation in log-linear models. Applied Statistics 33 (1): 38–44.
Burnham, K.P., and D.R. Anderson. 2002. Model selection and multimodel inference: A practical information-theoretic approach. 2nd ed. New York: Springer.
Carlin, B.P., and T.A. Louis. 2000. Bayes and empirical Bayes methods for data analysis. 2nd ed. Boca Raton: Chapman & Hall.
Casey, M., C. Gennings, W.H. Carter, V.C. Moser, and J.E. Simmons. 2004. Detecting interaction(s) and assessing the impact of component subsets in a chemical mixture using fixed-ratio mixture ray designs. Journal of Agricultural, Biological, and Environmental Statistics 9 (3): 339–361.
Christensen, E.R., and N. Nyholm. 1984. Ecotoxicological assays with algae: Weibull dose-response curves. Environmental Science & Technology 18 (9): 713–718.
Cook, R.D., and C.L. Tsai. 1985. Residuals in nonlinear regression. Biometrika 72 (1): 23–29.
Crofton, K.M., E.S. Craft, J.M. Hedge, C. Gennings, J.E. Simmons, R.A. Carchman, W.H. Carter, and M.J. DeVito. 2005. Thyroid-hormone-disrupting chemicals: Evidence for dose-dependent additivity or synergism. Environmental Health Perspectives 113 (11): 1549–1554.
Dette, H., N. Neumeyer, and K.F. Pilz. 2005. A note on nonparametric estimation of the effective dose in quantal bioassay. Journal of the American Statistical Association 100 (470): 503–510.
Dinse, G.E., and D.M. Umbach. 2011. Characterizing non-constant relative potency. Regulatory Toxicology and Pharmacology 60: 342–353.
Efron, B., and R. Tibshirani. 1993. An introduction to the bootstrap. Boca Raton: CRC Press.
EPA (Environmental Protection Agency). 2016. The ToxCast analysis pipeline: An R package for processing and modeling chemical screening data. Date of access: 12 December 2017. https://www.epa.gov/sites/production/files/2015-08/documents/pipeline_overview.pdf.
Fang, Q., W.W. Piegorsch, S.J. Simmons, X. Li, C. Chen, and Y. Wang. 2015. Bayesian model-averaged benchmark dose analysis via reparameterized quantal-response models. Biometrics 71 (4): 1168–1175.
Finney, D.J. 1971. Probit analysis. Cambridge: Cambridge University Press.
Freedman, D.A. 2006. On the so-called “Huber sandwich estimator” and “robust standard errors”. The American Statistician 60 (4): 299–302.
Guardabasso, V., D. Rodbard, and P.J. Munson. 1987. A model-free approach to estimation of relative potency in dose-response curve analysis. The American Journal of Physiology 252 (3): E357–E364.
Guardabasso, V., P.J. Munson, and D. Rodbard. 1988. A versatile method for simultaneous analysis of families of curves. The FASEB Journal 2 (3): 209–215.
Harrell, F.E. 2001. Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer.
Haseman, J.K., and M.D. Hogan. 1975. Selection of the experimental unit in teratology studies. Teratology 12 (2): 165–171.
Hertzberg, R.C., Y. Pan, R. Li, L.T. Haber, R.H. Lyles, D.W. Herr, V.C. Moser, and J.E. Simmons. 2013. A four-step approach to evaluate mixtures for consistency with dose addition. Toxicology 313: 134–144.
Hill, A.V. 1910. The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. The Journal of Physiology 40 (Suppl): iv–vii.
Hunt, D.L., and D. Bowman. 2004. A parametric model for detecting hormetic effects in developmental toxicity studies. Risk Analysis 24 (1): 65–72.
Kauermann, G., and R.J. Carroll. 2001. A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association 96 (456): 1387–1396.
Kelly, C., and J. Rice. 1990. Monotone smoothing with application to dose-response curves and assessment of synergism. Biometrics 46 (4): 1071–1085.
Kim, S.B., S.M. Bartell, and D.L. Gillen. 2016. Inference for the existence of hormetic dose-response relationships in toxicology studies. Biostatistics 17 (3): 523–536.
Margolin, B.H., N. Kaplan, and E. Zeiger. 1981. Statistical analysis of the Ames Salmonella/microsome test. Proceedings of the National Academy of Sciences of the United States of America 78 (6): 3779–3783.
Montgomery, D.C., E.A. Peck, and G.G. Vining. 2012. Introduction to linear regression analysis. 5th ed. Hoboken: Wiley.
Mood, A.M., and F.A. Graybill. 1963. Introduction to the theory of statistics. 2nd ed. New York: McGraw Hill.
Nottingham, Q.J., and J.B. Birch. 2000. A semiparametric approach to analyzing dose-response data. Statistics in Medicine 19: 389–404.
Piegorsch, W.W., and J.K. Haseman. 1991. Statistical methods for analyzing developmental toxicity data. Teratogenesis, Carcinogenesis, and Mutagenesis 11: 115–133.
Ramsay, J.O. 1988. Monotone regression splines in action. Statistical Science 3 (4): 425–461.
Reeve, R., and J.R. Turner. 2013. Pharmacodynamic models: Parameterizing the Hill equation, Michaelis-Menten, the logistic curve, and relationships among these models. Journal of Biopharmaceutical Statistics 23: 648–661.
Schwarz, G.E. 1978. Estimating the dimension of a model. Annals of Statistics 6 (2): 461–464.
Searle, S.R., G. Casella, and C.E. McCulloch. 2006. Variance components. New York: Wiley.
Seber, G.A.F. 1977. Linear regression analysis. New York: Wiley.
Seber, G., and C. Wild. 1989. Nonlinear regression. New York: Wiley.
Shockley, K.R. 2016. Estimating potency in high-throughput screening experiments by maximizing the rate of change in weighted Shannon entropy. Scientific Reports 6: 27897. https://doi.org/10.1038/srep27897.
Simmons, S.J., C. Chen, X. Li, Y. Wang, W.W. Piegorsch, Q. Fang, B. Hu, and G.E. Dunn. 2015. Bayesian model averaging for benchmark dose estimation. Environmental and Ecological Statistics 22: 5–16.
St. Laurent, R.T., and R.D. Cook. 1992. Leverage and superleverage in nonlinear regression. Journal of the American Statistical Association 87 (420): 985–990.
St. Laurent, R.T., and R.D. Cook. 1993. Leverage, local influence and curvature in nonlinear regression. Biometrika 80 (1): 99–106.
Stephens, M.A. 1974. EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association 69 (347): 730–737.
Wheeler, M.W., and A.J. Bailer. 2007. Properties of model-averaged BMDLs: A study of model averaging in dichotomous response risk estimation. Risk Analysis 27 (3): 659–670.
———. 2008. Model averaging software for dichotomous dose response risk estimation. Journal of Statistical Software 26 (5): 1–15. https://doi.org/10.18637/jss.v026.i05.
———. 2009. Comparing model averaging with other model selection strategies for benchmark dose estimation. Environmental and Ecological Statistics 16: 37–51.
Wilk, M.B., and R. Gnanadesikan. 1968. Probability plotting methods for the analysis of data. Biometrika 55 (1): 1–17.
Williams, D.A. 1982. Extra-binomial variation in logistic linear models. Applied Statistics 31 (2): 144–148.
Zorrilla, E.P. 1997. Multiparous species present problems (and possibilities) to developmentalists. Developmental Psychobiology 30 (2): 141–150.
Acknowledgments
This work was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Dinse, G.E., Umbach, D.M. (2018). Dose-Response Modeling. In: Rider, C., Simmons, J. (eds) Chemical Mixtures and Combined Chemical and Nonchemical Stressors. Springer, Cham. https://doi.org/10.1007/978-3-319-56234-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-56234-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56232-2
Online ISBN: 978-3-319-56234-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)