Abstract
Much progress has been made in the area of adaptive designs for clinical trials. However, little has been done regarding adaptive designs to identify optimal treatment strategies in animal studies. Motivated by an animal study of a novel strategy for treating strokes, we propose a Bayesian multi-stage cost-effectiveness design to simultaneously identify the optimal dose and determine the therapeutic treatment window for administrating the experimental agent. We consider a non-monotonic pattern for the dose–schedule–efficacy relationship and develop an adaptive shrinkage algorithm to assign more cohorts to admissible strategies. We conduct simulation studies to evaluate the performance of the proposed design by comparing it with two standard designs. These simulation studies show that the proposed design yields a significantly higher probability of selecting the optimal strategy, while it is generally more efficient and practical in terms of resource usage.
Original language | English (US) |
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Pages (from-to) | 1219-1229 |
Number of pages | 11 |
Journal | Statistical Methods in Medical Research |
Volume | 27 |
Issue number | 4 |
DOIs | |
State | Published - Apr 1 2018 |
Keywords
- Admissible set
- Bayesian approach
- animal study
- cost-effectiveness design
- multi-stage design
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management
MD Anderson CCSG core facilities
- Biostatistics Resource Group