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Optimal dose-finding designs with correlated continuous and discrete responses.

Valerii Fedorov1, Yuehui Wu, Rongmei Zhang

  • 1Research Statistics Unit, Biomedical Data Sciences, GlaxoSmithKline, PO Box 5089, Collegeville, PA 19426, USA.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Clinical trials often involve multiple simultaneous endpoints, including efficacy and toxicity.
  • Evaluating mixed continuous and categorical responses presents analytical challenges in dose-finding studies.

Purpose of the Study:

  • To develop and evaluate optimal experimental designs for dose-finding clinical studies with bivariate responses.
  • To compare the efficiency and practicality of two-stage versus fully adaptive designs.

Main Methods:

  • Utilized a bivariate probit dose-response model for mixed continuous and categorical data.
  • Investigated locally optimal, two-stage optimal, and fully adaptive designs.
  • Assessed design performance through simulation studies under various constraints.

Main Results:

  • Two-stage adaptive designs demonstrated comparable or superior efficiency to fully adaptive designs with moderate initial sample sizes.
  • Simulations confirmed the performance of the proposed designs.

Conclusions:

  • Two-stage adaptive designs offer a practical and efficient approach for dose-finding studies with bivariate outcomes.
  • These designs are easier to implement compared to fully adaptive methods, making them valuable for clinical practice.