  Clinical research and drug development in orphan diseases is challenging,
since large-scale randomized studies are difficult to conduct. Formally
synthesizing the evidence is therefore of great value, yet this is rarely done
in the drug approval process. Phase III designs that make better use of phase
II data can facilitate drug development in orphan diseases.
  A Bayesian meta-analytic approach is used to inform the phase III study with
phase II data. It is particularly attractive, since uncertainty of
between-trial heterogeneity can be dealt with probabilistically, which is
critical if the number of studies is small. Furthermore, it allows quantifying
and discounting the phase II data through the predictive distribution relevant
for phase III. A phase III design is proposed which uses the phase II data and
considers approval based on a phase III interim analysis. The design is
illustrated with a non-inferiority case study from an FDA approval in herpetic
keratitis (an orphan disease). Design operating characteristics are compared to
those of a traditional design, which ignores the phase II data.
  An analysis of the phase II data reveals good but insufficient evidence for
non-inferiority, highlighting the need for a phase III study. For the phase III
study supported by phase II data, the interim analysis is based on half of the
patients. For this design, the meta-analytic interim results are conclusive and
would justify approval. In contrast, based on the phase III data only, interim
results are inconclusive and would require further evidence.
  To accelerate drug development for orphan diseases, innovative study designs
and appropriate methodology are needed. Taking advantage of randomized phase II
data when analyzing phase III studies looks promising because the evidence from
phase II supports informed decision making. The implementation of the Bayesian
design is straightforward.
