  Incorporating historical information into the design and analysis of a new
clinical trial has been the subject of much recent discussion. For example, in
the context of clinical trials of antibiotics for drug resistant infections,
where patients with specific infections can be difficult to recruit, there is
often only limited and heterogeneous information available from the historical
trials. To make the best use of the combined information at hand, we consider
an approach based on the multiple power prior which allows the prior weight of
each historical study to be chosen adaptively by empirical Bayes. This choice
of weight has advantages in that it varies commensurably with differences in
the historical and current data and can choose weights near 1 if the data from
the corresponding historical study are similar enough to the data from the
current study. Fully Bayesian approaches are also considered. The methods are
applied to data from antibiotics trials. An analysis of the operating
characteristics in a binomial setting shows that the proposed empirical Bayes
adaptive method works well, compared to several alternative approaches,
including the meta-analytic prior.

Methodology