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Decision‐Theoretic Designs for Phase II Clinical Trials Allowing for Competing Studies
Author(s) -
Stallard Nigel
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00047
Subject(s) - frequentist inference , bayesian probability , computer science , clinical trial , decision theory , clinical study design , drug development , phase (matter) , function (biology) , biometrics , risk analysis (engineering) , operations research , econometrics , mathematical optimization , management science , bayesian inference , artificial intelligence , medicine , mathematics , statistics , economics , drug , biology , pharmacology , evolutionary biology , chemistry , organic chemistry , pathology
Summary This article describes an approach to optimal design of phase II clinical trials using Bayesian decision theory. The method proposed extends that suggested by Stallard (1998, Biometrics 54 , 279–294) in which designs were obtained to maximize a gain function including the cost of drug development and the benefit from a successful therapy. Here, the approach is extended by the consideration of other potential therapies, the development of which is competing for the same limited resources. The resulting optimal designs are shown to have frequentist properties much more similar to those traditionally used in phase II trials.