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Statistics in Biosciences

, Volume 10, Issue 1, pp 184–201 | Cite as

Optimal Benchmark for Evaluating Drug-Combination Dose-Finding Clinical Trials

  • Beibei GuoEmail author
  • Suyu Liu
Article
  • 91 Downloads

Abstract

Numerous dose-finding methods have been proposed for drug-combination trials. A head-to-head comparison of the performance of these designs is difficult and often not very meaningful because different designs use different models and decision rules that often require judicious calibration to obtain good small-sample performance. It is desirable to have a general benchmark that can be used to evaluate the absolute performance of combination dose-finding designs. In this article, we propose an optimal nonparametric benchmark for evaluating drug-combination dose-finding methods, which provides an upper bound of accuracy beyond which further improvements are generally not achievable without making parametric assumptions of the dose-toxicity relationship. Our method is based on a new concept called critical information, which provides an upper bound on the information that we could possibly learn from patients while explicitly accounting for the partial order of the dose combinations, a fundamental feature of drug-combination trials. Our numerical study shows that the proposed benchmark provides a sharp upper bound that is useful for evaluating the performance of combination dose-finding designs.

Keywords

Combination trials Dose finding Partial order Optimal benchmark Upper limit 

Supplementary material

12561_2017_9204_MOESM1_ESM.pdf (46 kb)
Supplementary material 1 (pdf 46 KB)

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Copyright information

© International Chinese Statistical Association 2017

Authors and Affiliations

  1. 1.Department of Experimental StatisticsLouisiana State UniversityBaton RougeUSA
  2. 2.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA

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