  We propose a dynamic allocation procedure that increases power and efficiency
when measuring an average treatment effect in sequential randomized trials.
Subjects arrive iteratively and are either randomized or paired via a matching
criterion to a previously randomized subject and administered the alternate
treatment. We develop estimators for the average treatment effect that combine
information from both the matched pairs and unmatched subjects as well as an
exact test. Simulations illustrate the method's higher efficiency and power
over competing allocation procedures in both controlled scenarios and
historical experimental data.
