Bregantini D, Schmitt LHM, Thijssen JJJ. A Bayesian change-point detection approach to the economic evaluation of risky projects: an application to healthcare technology assessment. J R Stat Soc Ser A Stat Soc. 2024 Apr;187(2):474-6. doi: 10.1093/jrsssa/qnad129


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 proposed 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 thresholds. 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.

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