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SUMMARY:Adaptive dose-finding designs to identify multiple doses that achi
 eve multiple response targets - Adrian Mander\, MRC Biostatistics Unit Hub
  for Trials Methodology Research
DTSTART:20141021T181500Z
DTEND:20141021T203000Z
UID:TALK48146@talks.cam.ac.uk
CONTACT:Peter Watson
DESCRIPTION:Within drug development it is crucial to find the right dose t
 hat is going to be safe and efficacious\, this is often done within early 
 phase II clinical trials. The aim of the dose-finding trial  is to underst
 and the relationship between the dose of drug and the potential effect of 
 the drug. Increasingly\, adaptive designs are being used in this area beca
 use they allow greater flexibility for dose exploration as compared to tra
 ditional fixed dose designs. \n\nAn adaptive dose-finding design usually a
 ssumes a true non-linear dose-response model and select doses that either 
 maximise the determinant of information matrix of the design (D-optimality
 ) or minimise the variance of the predicted dose that gives a targeted res
 ponse. Our design extends the predicted dose methodology\, in a limited nu
 mber of patients (40)\, to finding two targeted doses: a minimally effecti
 ve dose\; and a therapeutic. In our trial doses are assumed to be given in
 travenously so theoretically doses are continuous and the response is a no
 rmally distributed continuous outcome. \n\nOur design has an initial learn
 ing phase where pairs of patients are assigned to five pre-assigned doses.
  The next phase is fully sequential with an interim analysis after each pa
 tient to determine the choice of dose based on the optimality criterion to
  minimise the determinant of the covariance of the estimated target doses.
  The dose-choice algorithm assumes that a specific parametric dose-respons
 e model is the true relationship\, and so a variety of models are consider
 ed at the interim and human judgement involved in the overall decision. I 
 will also describe some of the discussions of the dose-decision making mee
 tings.\n
LOCATION:MRC Cognition and Brain Sciences Unit
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