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Compromise design for combination experiment of two drugs

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  • Huang, Hengzhen
  • Chen, Xueping

Abstract

Preclinical experiment on two-drug combination is a stepping stone to multi-drug combination studies. Experimental designs have been proposed in the literature to test the presence of synergism between the combined drugs. However, a design that is efficient for synergy testing is not necessarily desirable for dose–response modeling and the latter is important for future development on drug interaction analysis. This work proposes an experimental design, called a compromise design to meet the dual requirements on synergy testing and dose–response modeling. The key idea of the design is to spread the design points uniformly on a pair of design regions where synergy testing and dose–response modeling are respectively carried out. Simulations and two illustrative examples are given to demonstrate the usefulness of the compromise design. In the illustrative examples, the good balance of the proposed design is visualized by 2-D projections of the design points. The simulation results indicate that the compromise design performs satisfactorily in terms of both testing power and model prediction accuracy.

Suggested Citation

  • Huang, Hengzhen & Chen, Xueping, 2021. "Compromise design for combination experiment of two drugs," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
  • Handle: RePEc:eee:csdana:v:157:y:2021:i:c:s0167947320302413
    DOI: 10.1016/j.csda.2020.107150
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    References listed on IDEAS

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    1. Almohaimeed, B. & Donev, A.N., 2014. "Experimental designs for drug combination studies," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1077-1087.
    2. Maiying Kong & J. Jack Lee, 2006. "A Generalized Response Surface Model with Varying Relative Potency for Assessing Drug Interaction," Biometrics, The International Biometric Society, vol. 62(4), pages 986-995, December.
    3. Yu, Jun & Kong, Xiangshun & Ai, Mingyao & Tsui, Kwok Leung, 2018. "Optimal designs for dose–response models with linear effects of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 217-228.
    4. Holland-Letz, T. & Kopp-Schneider, A., 2018. "Optimal experimental designs for estimating the drug combination index in toxicology," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 182-193.
    5. Wiens, Douglas P., 1991. "Designs for approximately linear regression: two optimality properties of uniform designs," Statistics & Probability Letters, Elsevier, vol. 12(3), pages 217-221, September.
    6. Tian, Guo-Liang & Fang, Hong-Bin & Tan, Ming & Qin, Hong & Tang, Man-Lai, 2009. "Uniform distributions in a class of convex polyhedrons with applications to drug combination studies," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1854-1865, September.
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    Cited by:

    1. Wiens, Douglas P., 2021. "Robust designs for dose–response studies: Model and labelling robustness," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. Yu, Jun & Meng, Xiran & Wang, Yaping, 2023. "Optimal designs for semi-parametric dose-response models under random contamination," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).

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