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SUMMARY:Optimal data combination in seamless Phase II/III clinical trials 
 - Lisa Hampson\, Lancaster University.
DTSTART:20120619T100000Z
DTEND:20120619T113000Z
UID:TALK37218@talks.cam.ac.uk
CONTACT:Dr Jack Bowden
DESCRIPTION:We consider seamless Phase II/III clinical trials which compar
 e K treatments against a common control in stage 1 and select the most pro
 mising for further testing against control in stage 2. Such a trial requir
 es careful upfront planning if it is to win regulatory acceptance as a piv
 otal study. For seamless trials to be attractive\, this increased planning
  should be offset by efficiency gains made possible because data accumulat
 ed across the study are combined to make a final decision on the efficacy 
 of the selected treatment. We derive optimal versions of final decision ru
 les maximising power. This is a multivariate decision problem because prop
 erties of rules depend on a vector of means.\nRules with the correct famil
 ywise error rate maximising power for different configurations of means ar
 e found as solutions to Bayes decision problems. Different solutions are f
 ound as the shape of the mean vector changes but we find only small gains 
 in power are possible by making strong assumptions about the structure of 
 the mean vector. By studying procedures with optimal decision rules\, we a
 ssess the efficiency of alternative proposals\, namely closed testing proc
 edures based on p-value combination rules\, and rules using only data on t
 he selected treatment and control for final decisions. For procedures with
  efficient decision rules\, we find that Phase II observations on the sele
 cted treatment and control retain between 22-98% of their value as Phase I
 II observations. Thus\, efficient seamless designs can offer large savings
  in sample size which may have important implications\, for example\, for 
 the feasibility of trials in rare diseases.\n
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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