Name
Abstract | ||
|---|---|---|
We focus on the design of multiarm multistage (MAMS) clinical trials, using ideas from simulation optimization, biostatistics, and health economics. From a trial design perspective, we build on the trend of comparing multiple treatments with a single control by allowing for more than two arms in a trial, and we allow for arbitrarily many stages of sampling by using a diffusion approximation that allows for adaptive stopping rules. From a simulation perspective, our techniques extend the correlated knowledge-gradient concept, which has been used in one-stage lookahead (knowledge gradient) procedures, to Bayesian fully sequential selection procedures. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WSC.2016.7822401 | Winter Simulation Conference |
Field | DocType | ISSN |
Econometrics,Sequential selection,Mathematical optimization,Numerical models,Simulation,Computer science,Sampling (statistics),Multiple treatments,Bayesian probability | Conference | 0891-7736 |
ISBN | Citations | PageRank |
978-1-5090-4484-9 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ozge Yapar | 1 | 0 | 0.34 |
| Noah Gans | 2 | 613 | 66.60 |
| Stephen E. Chick | 3 | 1127 | 152.40 |