  The reproducibility crisis has led to an increasing number of replication
studies being conducted. Sample sizes for replication studies are often
calculated using conditional power based on the effect estimate from the
original study. However, this approach is not well suited as it ignores the
uncertainty of the original result. Bayesian methods are used in clinical
trials to incorporate prior information into power calculations. We propose to
adapt this methodology to the replication framework and promote the use of
predictive instead of conditional power in the design of replication studies.
Moreover, we describe how extensions of the methodology to sequential clinical
trials can be tailored to replication studies. Conditional and predictive power
calculated at an interim analysis are compared and we argue that predictive
power is a useful tool to decide whether to stop a replication study
prematurely. A recent project on the replicability of social sciences is used
to illustrate the properties of the different methods.
