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A Bayesian Approach to Assessing the Superiority of a Dose Combination
Author(s) -
Miller Melinda A.,
Seaman John W.
Publication year - 1998
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/(sici)1521-4036(199804)40:1<43::aid-bimj43>3.0.co;2-j
Subject(s) - frequentist inference , bayesian probability , markov chain monte carlo , computer science , machine learning , bayesian inference , econometrics , artificial intelligence , mathematics
One prevalent goal within clinical trials is to determine whether or not a combination of two drugs is more effective than each of its components. Many researchers have addressed this issue for fixed‐dose combination trials, using frequentist hypothesis testing techniques. In addition, several of these have incorporated prior information from sources such as Phase II trials or expert opinions. The Bayesian approach to the general selection problem naturally accomodates the need to utilize such information. It is useful in the dose combination problem because it does not rely on a nuisance parameter that affects the power of frequentist procedures. We show that hierarchical Bayesian methods may be easily applied to this problem, yielding the probability that a drug combination is superior to its components. Moreover, we present methods that may be implemented using readily available software for numerical integration as well as ones that incorporate Markov Chain Monte Carlo methods.