The growing role of Bayesian methods in clinical trial design and analysis

Abstract

Bayesian methods are increasingly used in clinical trials because of their flexibility and ability to incorporate prior knowledge into data analysis. Unlike frequentist approaches, which rely solely on current trial data, Bayesian methods combine prior information – such as data from earlier studies or expert opinion – with observed data to update the probability of a hypothesis. This dynamic updating process, based on Bayes’ theorem, provides a more intuitive framework for decision-making, particularly in adaptive trials or when sample sizes are small. Bayesian methods excel in handling complex problems such as multiple endpoints or subgroup analyses and allow continuous updates as new data become available. Key advantages include the incorporation of prior information, direct probability-based interpretations of results and greater adaptability compared with frequentist approaches. Applications in clinical trials include: adaptive designs, where trial parameters may be modified based on interim data; efficient use of prior information to reduce sample sizes; probabilistic decision-making to guide trial progress; and enhanced reliability in rare diseases or small trials. By offering a robust, intuitive framework for analysing trial data, Bayesian methods address the complexities of modern clinical research, improving efficiency, adaptability and resource utilization while supporting more informed regulatory and development decisions.