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Optimal approximate designs for comparison with control in dose-escalation studies

Author

Listed:
  • Samuel Rosa

    (Comenius University in Bratislava)

  • Radoslav Harman

    (Comenius University in Bratislava)

Abstract

Consider an experiment, in which a new drug is tested for the first time on human subjects, namely healthy volunteers. Such experiments are often performed as dose-escalation studies: a set of increasing doses is preselected; individuals are grouped into cohorts; and in each cohort, dose number i can be administered only if dose number $$i-1$$ i - 1 has already been tested in the previous cohort. If an adverse effect of a dose is observed, the experiment is stopped, and thus, no subjects are exposed to higher doses. In this paper, we assume that the response is affected both by the dose or placebo effects and by the cohort effects. We provide optimal approximate designs for estimating the effects of the drug doses compared with the placebo with respect to selected optimality criteria (E-, MV- and LV-optimality). In particular, we prove the optimality of the so-called Senn designs with respect to all of the studied optimality criteria, and we provide optimal extensions of these designs for selected criteria.

Suggested Citation

  • Samuel Rosa & Radoslav Harman, 2017. "Optimal approximate designs for comparison with control in dose-escalation studies," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 638-660, September.
    • Handle: RePEc:spr:testjl:v:26:y:2017:i:3:d:10.1007_s11749-017-0529-3
    DOI: 10.1007/s11749-017-0529-3
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    References listed on IDEAS

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    1. Samuel Rosa & Radoslav Harman, 2016. "Optimal approximate designs for estimating treatment contrasts resistant to nuisance effects," Statistical Papers, Springer, vol. 57(4), pages 1077-1106, December.
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    Cited by:

    1. Samuel Rosa, 2019. "Equivalence of weighted and partial optimality of experimental designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 719-732, August.

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