  In clinical trials, there often exist multiple historical studies for the
same or related treatment investigated in the current trial. Incorporating
historical data in the analysis of the current study is of great importance, as
it can help to gain more information, improve efficiency, and provide a more
comprehensive evaluation of treatment. Enlightened by the unit information
prior (UIP) concept in the reference Bayesian test, we propose a new
informative prior called UIP from an information perspective that can
adaptively borrow information from multiple historical datasets. We consider
both binary and continuous data and also extend the new UIP methods to linear
regression settings. Extensive simulation studies demonstrate that our method
is comparable to other commonly used informative priors, while the
interpretation of UIP is intuitive and its implementation is relatively easy.
One distinctive feature of UIP is that its construction only requires summary
statistics commonly reported in the literature rather than the patient-level
data. By applying our UIP methods to phase III clinical trials for
investigating the efficacy of memantine in Alzheimer's disease, we illustrate
its ability of adaptively borrowing information from multiple historical
datasets in the real application.
