Bayesian probability of success for clinical trials using historical data.

TitleBayesian probability of success for clinical trials using historical data.
Publication TypeJournal Article
Year of Publication2015
AuthorsIbrahim, Joseph G., Ming-Hui Chen, Mani Lakshminarayanan, Guanghan F. Liu, and Joseph F. Heyse
JournalStat Med
Date Published2015 Jan 30
KeywordsAged, Analysis of Variance, Antibodies, Viral, Bayes Theorem, Clinical Trials, Phase II as Topic, Clinical Trials, Phase III as Topic, Computer Simulation, Data Interpretation, Statistical, Decision Making, Female, Herpes Zoster, Herpes Zoster Vaccine, Herpesvirus 3, Human, Humans, Likelihood Functions, Linear Models, Logistic Models, Male, Probability

Developing sophisticated statistical methods for go/no-go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics. A new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang-Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre-market or post-market trials.

Alternate JournalStat Med
Original PublicationBayesian probability of success for clinical trials using historical data.
PubMed ID25339499
PubMed Central IDPMC4676938
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States