Skip to main content
Log in

A comparative study of the dose-response analysis with application to the target dose estimation

  • Published:
Journal of Statistical Theory and Practice Aims and scope Submit manuscript

Abstract

With a quantal response, the dose-response relation is summarized by the response probability function (RPF) that provides probabilities of the response being reacted as a function of dose levels. In the dose-response analysis (DRA), it is often of primary interest to find a dose at which targeted response probability is attained, which we call target dose (TD). The estimation of the TD clearly depends on the underlying RPF structure. In this article, we provide a comparative analysis of some of the existing and newly proposed RPF estimation methods with particular emphasis on TD estimation. Empirical performances based on simulated data are presented to compare the existing and newly proposed methods. Nonparametric models based on a sequence of Bernstein polynomials are found to be robust against model misspecification. The methods are also illustrated using data obtained from a toxicological study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ahn, Η., and J. J. Chen. 1997. Tree-structured logistic model for over-dispersed binomial data with application to modelling developmental effects. Biometrics 53 (2):435–55.

    Article  MATH  Google Scholar 

  • Bailer, A. J., R. B. Noble, and M. W. Wheeler. 2005. Model uncertainty and risk estimation for experimental studies of quantal responses. Risk Analysis 25 (2):291–99.

    Article  Google Scholar 

  • Bornkamp, B., and K. Ickstadt. 2009. Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis. Biometrics 65 (1):198–205.

    Article  MathSciNet  MATH  Google Scholar 

  • Bretz, F., J. C. Pinheiro, and M. Branson. 2005. Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics 61 (3):738–48.

    Article  MathSciNet  MATH  Google Scholar 

  • Brown, B. M., and S. X. Chen. 1999. Beta-bernstein smoothing for regression curves with compact support. Scandinavian Journal of Statistics 26 (1):47–59.

    Article  MathSciNet  MATH  Google Scholar 

  • Carnicer, J. M., and J. M. Peña. 1993. Shape preserving representations and optimality of the bernstein basis. Advances in Computational Mathematics 1 (2):173–96.

    Article  MathSciNet  MATH  Google Scholar 

  • Cornfield, J. 1977. Carcinogenic risk assessment. Science 198 (4318):693–99.

    Article  Google Scholar 

  • Cox, C. 1987. Threshold dose-response models in toxicology. Biometrics 43 (4):511–23.

    Article  Google Scholar 

  • 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):503–10.

    Article  MathSciNet  MATH  Google Scholar 

  • Farouki, R. T. 2012. The bernstein polynomial basis: A centennial retrospective. Computer Aided Geometric Design 29 (6):379–419.

    Article  MathSciNet  MATH  Google Scholar 

  • Gasparini, M., and J. Eisele, 2000. A curve-free method for phase I clinical trials. Biometrics 56 (2):609–15.

    Article  MathSciNet  MATH  Google Scholar 

  • Hall, P., and L.-S. Huang. 2001. Nonparametric kernel regression subject to monotonicity constraints. Annals of Statistics 29 (3):624–47.

    Article  MathSciNet  MATH  Google Scholar 

  • Holson, J., T. Gaines, C. Nelson, J. LaBorde, D. Gaylor, D. Sheehan, and J. Young 1992. Developmental toxicity of 2, 4, 5-trichlorophenoxyacetic acid (2, 4, 5-T) I. Multireplicated dose- response studies in four inbred strains and one outbred stock of mice. Toxicological Sciences 19 (2):286–97.

    Article  Google Scholar 

  • Hyndman, R. J. 1996. Computing and graphing highest density regions. American Statistician 50 (2):120–26.

    Google Scholar 

  • Lorentz, G. G. 2012. Bernstein polynomials. 2nd ed. New Yok, NY: AMS Chelsea Publishing.

    MATH  Google Scholar 

  • Macdougall, J. 2006. Analysis of dose-response studies-Emax model. In Dose finding in drug development, ed. N. Ting, 127–145. New York, NY: Springer.

    Chapter  Google Scholar 

  • Mammen, E., J. Marron, Β. Turlach, Μ. Wand, et al. 2001. A general projection framework for constrained smoothing. Statistical Science 16 (3):232–48.

    Article  MathSciNet  MATH  Google Scholar 

  • McKay, C. S., and S. K. Ghosh. 2011. A variable selection approach to monotonic regression with bernstein polynomials. Journal of Applied Statistics 38 (5):961–76.

    Article  MathSciNet  Google Scholar 

  • Mukerjee, H. 1988. Monotone nonparametric regression. Annals of Statistics 16 (2):741–50.

    Article  MathSciNet  MATH  Google Scholar 

  • Müller, H.-G., and T. Schmitt. 1988. Kernel and probit estimates in quantal bioassay. Journal of the American Statistical Association 83 (403):750–59.

    MathSciNet  MATH  Google Scholar 

  • Perron, F., and K. Mengersen. 2001. Bayesian nonparametric modeling using mixtures of triangular distributions. Biometrics 57 (2):518–28.

    Article  MathSciNet  MATH  Google Scholar 

  • Peto, R. 1977. Epidemiology, multistage models, and short-term mutagenicity tests. Origins of Human Cancer 4:1403–28.

    Google Scholar 

  • Pinheiro, J. C., F. Bretz, and M. Branson. 2006. Analysis of dose-response studies: Modeling approaches. In dose finding in drug development, ed. N. Ting, 146–171. New York, NY: Springer.

    Chapter  Google Scholar 

  • Rai, K., and J. Van Ryzin. 1979. Risk assessment of toxic environmental substances using a generalized multi-hit dose response model. In Energy and Health, ed. Ν. Ε. Breslow, 99–117. Philadelphia, PA: SIAM Press.

    Google Scholar 

  • Ramsay, J. O. 1988. Monotone regression splines in action. Statistical Science 3 (4):425–41.

    Article  Google Scholar 

  • Shao, J., and D. Tu. 2012. The jackknife and bootstrap. New York, NY: Springer Science & Business Media.

    MATH  Google Scholar 

  • Stadtmüller, U. 1986. Asymptotic properties of nonparametric curve estimates. Periodica Mathematica Hungarica 17 (2):83–108.

    Article  MathSciNet  MATH  Google Scholar 

  • Tenbusch, A. 1997. Nonparametric curve estimation with bernstein estimates. Metrika 45 (1):1–30.

    Article  MathSciNet  MATH  Google Scholar 

  • Thompson Jr., W., and R. Funderlic. 1981. A simple threshold model for the classical bioassay problem. Measurement of Risks 521–533. New York, NY: Springer.

    Chapter  Google Scholar 

  • Ting, N. 2006. Dose finding in drug development. New York, NY: Springer.

    Book  MATH  Google Scholar 

  • Wang, J. and S. Ghosh. 2012. Shape restricted nonparametric regression with bernstein polynomials. Computational Statistics & Data Analysis 56 (9):2729–41.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, X., and F. Li. 2008. Isotonic smoothing spline regression. Journal of Computational and Graphical Statistics 17 (1):21–37.

    Article  MathSciNet  Google Scholar 

  • White, H. 1982. Maximum likelihood estimation of misspecified models. Econometrica 50 (1):1–25.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seung Jun Shin.

Additional information

Color versions of one or more of the figures in the article can be found online at https://doi.org/www.tandfonline.com/ujsp.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shin, S.J., Ghosh, S.K. A comparative study of the dose-response analysis with application to the target dose estimation. J Stat Theory Pract 11, 145–162 (2017). https://doi.org/10.1080/15598608.2016.1261260

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1080/15598608.2016.1261260

Keywords

AMS Subject Classification

Navigation