Interval designs have recently attracted enormous attention due to their simplicity, desirable properties, and superior performance. We study random-walk and parallel-crossing Bayesian optimal interval designs for dose finding in drug-combination trials. The entire dose-finding procedures of these two designs are nonparametric (or model-free), which are thus robust and also do not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. Simulation studies demonstrate the finite-sample performance of the proposed methods under various scenarios. Both designs are illustrated with a phase I two-agent dose-finding trial in prostate cancer.
CITATION STYLE
Lin, R., & Yin, G. (2017). Random walk and parallel crossing bayesian optimal interval design for dose finding with combined drugs. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (pp. 21–35). Springer Singapore. https://doi.org/10.1007/978-981-10-0126-0_3
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