Please use this identifier to cite or link to this item: https://doi.org/10.1002/sim.2322
DC FieldValue
dc.titleDecision-theoretic designs for dose-finding clinical trials with multiple outcomes
dc.contributor.authorFan, S.K.
dc.contributor.authorWang, Y.-G.
dc.date.accessioned2014-10-28T05:11:09Z
dc.date.available2014-10-28T05:11:09Z
dc.date.issued2006-05-30
dc.identifier.citationFan, S.K., Wang, Y.-G. (2006-05-30). Decision-theoretic designs for dose-finding clinical trials with multiple outcomes. Statistics in Medicine 25 (10) : 1699-1714. ScholarBank@NUS Repository. https://doi.org/10.1002/sim.2322
dc.identifier.issn02776715
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105080
dc.description.abstractA decision-theoretic framework is proposed for designing sequential dose-finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose-finding studies involving k doses, the computational complexity is the same as the bandit problem with k-dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1002/sim.2322
dc.sourceScopus
dc.subjectBandit process
dc.subjectOptimality
dc.subjectSequential clinical trials
dc.subjectSub-optimality
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1002/sim.2322
dc.description.sourcetitleStatistics in Medicine
dc.description.volume25
dc.description.issue10
dc.description.page1699-1714
dc.description.codenSMEDD
dc.identifier.isiut000237869100008
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

4
checked on Aug 17, 2022

WEB OF SCIENCETM
Citations

4
checked on Aug 17, 2022

Page view(s)

110
checked on Aug 18, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.