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SUMMARY:Response-adaptive dose-finding under model uncertainty - Bretz\, F
  (Novartis)
DTSTART:20110816T130000Z
DTEND:20110816T133000Z
UID:TALK32379@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:In pharmaceutical drug development\, dose-finding studies are 
 of critical importance because both safety and clinically relevant efficac
 y have to be demonstrated for a specific dose of a new compound before mar
 ket authorization. Motivated by a real dose-finding study\, we propose res
 ponse-adaptive designs addressing two major challenges in dose-finding stu
 dies: uncertainty about the dose-response models and large variability in 
 parameter estimates. To allocate new cohorts of patients in an ongoing stu
 dy\, we use optimal designs that are robust under model uncertainty. In ad
 dition\, we use a Bayesian shrinkage approach to stabilize the parameter e
 stimates over the successive interim analyses used in the adaptations. Thi
 s approach allows us to calculate updated parameter estimates and model pr
 obabilities that can then be used to calculate the optimal design for subs
 equent cohorts. The resulting designs are hence robust with respect to mod
 el misspecification and additionally can ef ficiently adapt to the informa
 tion accrued in an ongoing study. We focus on adaptive designs for estimat
 ing the minimum effective dose\, although alternative optimality criteria 
 or mixtures thereof could be used\, enabling the design to address multipl
 e objectives. In an extensive simulation study\, we investigate the operat
 ing characteristics of the proposed method under a variety of scenarios.\n
LOCATION:Seminar Room 1\, Newton Institute
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