Bayesian designs of phase II oncology trials to select maximum effective dose assuming monotonic dose-response relationship
1 Department of Experimental Statistics, , Baton Rouge, 70803 LA, USA
2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1411, Houston, 77030 TX, USA
BMC Medical Research Methodology 2014, 14:95 doi:10.1186/1471-2288-14-95Published: 29 July 2014
For many molecularly targeted agents, the probability of response may be assumed to either increase or increase and then plateau in the tested dose range. Therefore, identifying the maximum effective dose, defined as the lowest dose that achieves a pre-specified target response and beyond which improvement in the response is unlikely, becomes increasingly important. Recently, a class of Bayesian designs for single-arm phase II clinical trials based on hypothesis tests and nonlocal alternative prior densities has been proposed and shown to outperform common Bayesian designs based on posterior credible intervals and common frequentist designs. We extend this and related approaches to the design of phase II oncology trials, with the goal of identifying the maximum effective dose among a small number of pre-specified doses.
We propose two new Bayesian designs with continuous monitoring of response rates across doses to identify the maximum effective dose, assuming monotonicity of the response rate across doses. The first design is based on Bayesian hypothesis tests. To determine whether each dose level achieves a pre-specified target response rate and whether the response rates between doses are equal, multiple statistical hypotheses are defined using nonlocal alternative prior densities. The second design is based on Bayesian model averaging and also uses nonlocal alternative priors. We conduct simulation studies to evaluate the operating characteristics of the proposed designs, and compare them with three alternative designs.
In terms of the likelihood of drawing a correct conclusion using similar between-design average sample sizes, the performance of our proposed design based on Bayesian hypothesis tests and nonlocal alternative priors is more robust than that of the other designs. Specifically, the proposed Bayesian hypothesis test-based design has the largest probability of being the best design among all designs under comparison and the smallest probability of being an inadequate design, under sensible definitions of the best design and an inadequate design, respectively.
The use of Bayesian hypothesis tests and nonlocal alternative priors under ordering constraints between dose groups results in a robust performance of the design, which is thus superior to other common designs.