Format

Send to

Choose Destination
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.

Author information

1
Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
2
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
3
Cancer Research Informatics, Center for Clinical and Research and Informatics, NorthShore University HealthSystem, Evanston, IL 60201, USA.

Abstract

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.

KEYWORDS:

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

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center