A Bayesian change-point detection approach to the economic evaluation of risky projects : an application to health-care technology assessment

We propose a Bayesian hypothesis testing framework that allows for the assessment of evidence collected during a clinical trial about the cost-effectiveness of a health-care technology. The model exploits a Bayesian updating rule that makes the link between the evidence collected in clinical research and the expected payoffs of adoption to the health-care system. The framework takes into account the cost of decision errors in the payoff function, allowing the decision maker to compute the cost of taking a decision when evidence is far from the optimal decision triggers. We show, using a real-world cost-effectiveness study based on clinical trial evidence, how rules derived from a sequential adaptive design approach can lead to quicker decisions when compared to the Value of Information decision framework. Our application shows that a sequential approach has the potential to lead to quicker decisions, higher payoffs and better health outcomes.