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

TY - JOUR

T1 - A Bayesian change-point detection approach to the economic evaluation of risky projects

T2 - an application to health-care technology assessment

AU - Bregantini, Daniele

AU - Schmitt, Laetitia Helene Marie

AU - Thijssen, Jacco

N1 - © The Royal Statistical Society 2023

PY - 2023/11/22

Y1 - 2023/11/22

N2 - 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.

AB - 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.

U2 - 10.1093/jrsssa/qnad129

DO - 10.1093/jrsssa/qnad129

M3 - Article

SN - 0964-1998

JO - Journal of the Royal Statistical Society: Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)

ER -