  When a novel treatment has successfully passed phase I, different options to
design subsequent phase II trials are available. One approach is a single-arm
trial, comparing the response rate in the intervention group against a fixed
proportion. Another alternative is to conduct a randomized phase II trial,
comparing the new treatment with placebo or the current standard. A significant
problem arises in both approaches when the investigated patient population is
very heterogeneous regarding prognostic factors. For the situation that a
substantial dataset of historical controls exists, we propose an approach to
enhance the classic single-arm trial design by including matched control
patients. The outcome of the observed study population can be adjusted based on
the matched controls with a comparable distribution of known confounders. We
propose an adaptive two-stage design with the options of early stopping for
futility and recalculation of the sample size taking the matching rate, number
of matching partners, and observed treatment effect into account. The
performance of the proposed design in terms of type I error rate, power, and
expected sample size is investigated via simulation studies based on a
hypothetical phase II trial investigating a novel therapy for patients with
acute myeloid leukemia.
