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Bayesian Optimal Designs for Phase I Clinical Trials
Author(s) -
Haines Linda M.,
Perevozskaya Inna,
Rosenberger William F.
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 - 10.1111/1541-0420.00069
Subject(s) - optimal design , bayesian probability , equivalence (formal languages) , maximum tolerated dose , mathematical optimization , constraint (computer aided design) , computer science , bayesian experimental design , bayesian inference , mathematics , clinical trial , algorithm , medicine , bayesian statistics , machine learning , artificial intelligence , geometry , discrete mathematics , pathology
Summary . A broad approach to the design of Phase I clinical trials for the efficient estimation of the maximum tolerated dose is presented. The method is rooted in formal optimal design theory and involves the construction of constrained Bayesian c ‐ and D ‐optimal designs. The imposed constraint incorporates the optimal design points and their weights and ensures that the probability that an administered dose exceeds the maximum acceptable dose is low. Results relating to these constrained designs for log doses on the real line are described and the associated equivalence theorem is given. The ideas are extended to more practical situations, specifically to those involving discrete doses. In particular, a Bayesian sequential optimal design scheme comprising a pilot study on a small number of patients followed by the allocation of patients to doses one at a time is developed and its properties explored by simulation.