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SUMMARY:Bayesian enrichment strategies for randomized discontinuation tria
 ls - Rosner\, G (Johns Hopkins)
DTSTART:20110810T130000Z
DTEND:20110810T134500Z
UID:TALK32305@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:We propose optimal choice of the design parameters for random 
 discontinuation designs (RDD) using a Bayesian decision-theoretic approach
 . We consider applications of RDDs to oncology phase II studies evaluating
  activity of cytostatic agents. The design consists of two stages. The pre
 liminary open-label stage treats all patients with the new agent and ident
 i?es a possibly sensitive subpopulation. The subsequent second stage rando
 mizes\, treats\, follows\, and compares outcomes among patients in the ide
 nti?ed subgroup\, with randomization to either the new or a control treatm
 ent. Several tuning parameters characterize the design: the number of pati
 ents in the trial\, the duration of the preliminary stage\, and the durati
 on of follow-up after randomization. We de?ne a probability model for tumo
 r growth\, specify a suitable utility function\, and develop a computation
 al procedure for selecting the optimal tuning parameters.\n
LOCATION:Seminar Room 1\, Newton Institute
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