  Bayesian statistics plays a pivotal role in advancing medical science by
enabling healthcare companies, regulators, and stakeholders to assess the
safety and efficacy of new treatments, interventions, and medical procedures.
The Bayesian framework offers a unique advantage over the classical framework,
especially when incorporating prior information into a new trial with quality
external data, such as historical data or another source of co-data. In recent
years, there has been a significant increase in regulatory submissions using
Bayesian statistics due to its flexibility and ability to provide valuable
insights for decision-making, addressing the modern complexity of clinical
trials where frequentist trials are inadequate. For regulatory submissions,
companies often need to consider the frequentist operating characteristics of
the Bayesian analysis strategy, regardless of the design complexity. In
particular, the focus is on the frequentist type I error rate and power for all
realistic alternatives. This tutorial review aims to provide a comprehensive
overview of the use of Bayesian statistics in sample size determination in the
regulatory environment of clinical trials. Fundamental concepts of Bayesian
sample size determination and illustrative examples are provided to serve as a
valuable resource for researchers, clinicians, and statisticians seeking to
develop more complex and innovative designs.
