  Time-to-event endpoints show an increasing popularity in phase II cancer
trials. The standard statistical tool for such one-armed survival trials is the
one-sample log-rank test. Its distributional properties are commonly derived in
the large sample limit. It is however known from the literature, that the
asymptotical approximations suffer when sample size is small. There have
already been several attempts to address this problem. While some approaches do
not allow easy power and sample size calculations, others lack a clear
theoretical motivation and require further considerations. The problem itself
can partly be attributed to the dependence of the compensated counting process
and its variance estimator. For this purpose, we suggest a variance estimator
which is uncorrelated to the compensated counting process. Moreover, this and
other present approaches to variance estimation are covered as special cases by
our general framework. For practical application, we provide sample size and
power calculations for any approach fitting into this framework. Finally, we
use simulations and real world data to study the empirical type I error and
power performance of our methodology as compared to standard approaches.
