Enhancing Clinical Trial Analysis: The Role of Bayes Factors in Evaluating Drug Efficacy

Clinical trials are essential for evaluating new medical interventions. However, challenges such as increasing costs and enrolment difficulties often result in inadequate sample sizes. This makes it challenging to obtain reliable efficacy assessments using traditional frequentist methods. Bayesian statistics provides a superior solution to this problem by integrating prior knowledge and managing uncertainty effectively with limited data. In particular, Bayes factor serves as a key metric, offering several advantages over the conventional p-value: 1) explicit incorporation of prior evidence, 2) balancing the assessment of the null and alternative hypotheses, and 3) providing an intuitive probabilistic interpretation of the results. Using a theoretical framework and an empirical case study of acute inflammatory diseases of peripheral nerves, this paper shows that this method can enable early trial termination decisions in small samples and generate more robust efficacy evidence. This approach improves the efficiency of clinical trials and provides new ideas for future regulatory decision-making and post-market drug monitoring.