Bayesian Methods in Healthcare Decision-Making

This technical report discusses the application of Bayesian statistics in healthcare decision-making, particularly in health technology assessments (HTAs). It outlines how Bayesian methods can integrate historical and real-world data into survival and cost-effectiveness models, thereby enhancing the accuracy and transparency of treatment efficacy estimates. The report details the advantages of employing Bayesian approaches, especially in scenarios where data is limited, such as in rare diseases or initial reimbursement assessments. It highlights the challenges currently hindering the broader adoption of these methods, including complexity in model design and the need for specialized knowledge. The report also presents opportunities for utilizing Bayesian frameworks to improve the rigor of analyses and facilitate stakeholder engagement during regulatory processes. Furthermore, it emphasizes the potential for Bayesian models to provide more reliable extrapolations of survival outcomes from immature data, ultimately supporting more informed healthcare decisions.