Abstract
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.
Original language | English (US) |
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Title of host publication | Frontiers of Biostatistical Methods and Applications in Clinical Oncology |
Publisher | Springer Singapore |
Pages | 21-35 |
Number of pages | 15 |
ISBN (Electronic) | 9789811001260 |
ISBN (Print) | 9789811001246 |
DOIs | |
State | Published - Oct 3 2017 |
Externally published | Yes |
Keywords
- Bayesian method
- Dose finding
- Drug combination
- Interval design
- Random walk
ASJC Scopus subject areas
- General Medicine
- General Mathematics
- General Social Sciences