  Background
  A key requirement for a useful power calculation is that the calculation
mimic the data analysis that will be performed on the actual data, once it is
observed. Close approximations may be difficult to achieve using analytic
solutions, however, and thus Monte Carlo approaches, including both simulation
and bootstrap resampling, are often attractive. One setting in which this is
particularly true is cluster-randomized trial designs. However, Monte Carlo
approaches are useful in many additional settings as well. Calculating power
for cluster-randomized trials using analytic or simulation-based methods is
frequently unsatisfactory due to the complexity of the data analysis methods to
be employed and to the sparseness of data to inform the choice of important
parameters in these methods.
  Methods
  We propose that among Monte Carlo methods, bootstrap approaches are most
likely to generate data similar to the observed data. Means of implementation
are described.
  Results
  We demonstrate bootstrap power calculation for a cluster-randomized trial
with a survival outcome and a baseline observation period.
  Conclusions
  Bootstrap power calculation, a natural application of resampling methods,
provides a relatively simple solution to power calculation that is likely to be
the most accurate option. It has several important strengths. Notably, it is
simple to achieve fidelity to the proposed data analysis method and there is no
requirement for estimates from outside settings. We are not aware of bootstrap
power calculation being previously proposed or explored for cluster-randomized
trials. We demonstrate power calculations for a time-to-event outcome in a
cluster randomized trial setting, for which we are unaware of an analytic
alternative.
