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J R Stat Soc Ser C Appl Stat. 2014 Jan 1;63(1):159-173.

A Bayesian Dose-finding Design for Oncology Clinical Trials of Combinational Biological Agents.

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Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
Cancer Research Informatics, Center for Clinical and Research and Informatics, NorthShore University HealthSystem, Evanston, IL 60201, USA.


Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which efficacy and toxicity monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a dose-finding design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships.


Adaptive design; Biologically optimal dose combination; Change-point model; Dose finding; Drug combination; Non-monotonic pattern

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