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Robust designs for dose–response studies: Model and labelling robustness

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  • Wiens, Douglas P.

Abstract

Methods for the construction of dose–response designs are presented that are robust against possible model misspecifications and mislabelled responses. The asymptotic properties are studied, leading to asymptotically minimax designs that minimize the maximum – over neighbourhoods of both types of model inadequacies – value of the mean squared error of the predictions. Both sequential and adaptive approaches are studied. Finite sample simulations and examples illustrate the gains to be made by adaptivity.

Suggested Citation

  • Wiens, Douglas P., 2021. "Robust designs for dose–response studies: Model and labelling robustness," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000232
    DOI: 10.1016/j.csda.2021.107189
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    References listed on IDEAS

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    1. Huang, Hengzhen & Chen, Xueping, 2021. "Compromise design for combination experiment of two drugs," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. Magnus, Jan R., 1985. "On Differentiating Eigenvalues and Eigenvectors," Econometric Theory, Cambridge University Press, vol. 1(2), pages 179-191, August.
    3. Pengfei Li & Douglas P. Wiens, 2011. "Robustness of design in dose–response studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 215-238, March.
    4. Dette, Holger & Bretz, Frank & Pepelyshev, Andrey & Pinheiro, José, 2008. "Optimal Designs for Dose-Finding Studies," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1225-1237.
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