A practical Bayesian adaptive design incorporating data from historical controls.

TitleA practical Bayesian adaptive design incorporating data from historical controls.
Publication TypeJournal Article
Year of Publication2018
AuthorsPsioda, Matthew A., Mat Soukup, and Joseph G. Ibrahim
JournalStat Med
Date Published2018 Nov 30
KeywordsAdult, Bayes Theorem, Cardiovascular Diseases, Clinical Trials as Topic, Control Groups, Data Interpretation, Statistical, Diabetes Mellitus, Type 2, Humans, Hypoglycemic Agents, Linear Models, Research Design

In this paper, we develop the fixed-borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject-level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior-data conflict. If there is too much conflict between the new trial data and the historical control data, the prior information is discarded and the study proceeds to the final analysis opportunity at which time a noninformative prior is used for analysis. Otherwise, the trial is stopped early and the informative power prior is used for analysis. Simulation studies are used to calibrate the early stopping rule. The proposed design methodology seamlessly accommodates covariates in the statistical model, which the authors argue is necessary to justify borrowing information from historical controls. Implementation of the proposed methodology is straightforward for many common data models, including linear regression models, generalized linear regression models, and proportional hazards models. We demonstrate the methodology to design a cardiovascular outcomes trial for a hypothetical new therapy for treatment of type 2 diabetes mellitus and borrow information from the SAVOR trial, one of the earliest cardiovascular outcomes trials designed to assess cardiovascular risk in antidiabetic therapies.

Alternate JournalStat Med
Original PublicationA practical Bayesian adaptive design incorporating data from historical controls.
PubMed ID30033617
PubMed Central IDPMC6327964
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States