4. Probabilistic Sensitivity Analysis Using BCEA

This chapter delves into the application of probabilistic sensitivity analysis (PSA) in economic evaluations, focusing on its role in assessing the impact of model uncertainty on decision-making. The text begins by introducing PSA and its importance in health technology assessment, particularly in scenarios where the evidence base is limited or the statistical model fails to account for all relevant aspects. The chapter then explores PSA for parameter uncertainty, explaining how input parameters are considered as random quantities and how this randomness is propagated through the economic model to produce a distribution of decisions. The discussion includes the differences between Bayesian and frequentist approaches to PSA and the implications of these differences for decision-making. The chapter also covers PSA for structural uncertainty, which involves analyzing the influence of model assumptions on the optimal decision. Practical examples using the BCEA package in R are provided to illustrate the different tools available for understanding and reporting the impact of parametric uncertainty. The chapter concludes by emphasizing the importance of considering the uncertainty associated with decision-making in economic evaluations and the potential value of collecting additional information before making decisions. Readers will gain a comprehensive understanding of PSA techniques and their application in economic evaluations, as well as insights into the practical implementation of these techniques using the BCEA package.