A Bayesian dose finding design for oncology clinical trials of combinational biological agents
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, 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 design proposed has desirable operating characteristics in identifying the biologically optimal dose combination under various patterns of dose–toxicity and dose–efficacy relationships.