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In clinical trials, there often exist multiple historical studies for thesame or related treatment investigated in the current trial. Incorporatinghistorical data in the analysis of the current study is of great importance, asit can help to gain more information, improve efficiency, and provide a morecomprehensive evaluation of treatment. Enlightened by the unit informationprior (UIP) concept in the reference Bayesian test, we propose a newinformative prior called UIP from an information perspective that canadaptively borrow information from multiple historical datasets. We considerboth binary and continuous data and also extend the new UIP methods to linearregression settings. Extensive simulation studies demonstrate that our methodis comparable to other commonly used informative priors, while theinterpretation of UIP is intuitive and its implementation is relatively easy.One distinctive feature of UIP is that its construction only requires summarystatistics commonly reported in the literature rather than the patient-leveldata. By applying our UIP methods to phase III clinical trials forinvestigating the efficacy of memantine in Alzheimer's disease, we illustrateits ability of adaptively borrowing information from multiple historicaldatasets in the real application.

Unit Information Prior for Adaptive Information Borrowing from Multiple Historical Datasets