Proposal for fully sequential multiarm trials with correlated arms

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 t...

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Bibliographic Details
Main Authors: Yapar, Ozge, Gans, Noah, Chick, Stephen E.
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Clinical trials
Economics
Gallium nitride
Numerical models
Optimization
Online Access:Request full text
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Summary: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.
ISSN:1558-4305
DOI:10.1109/WSC.2016.7822401