In a probabilistic sensitivity analysis (PSA) of a cost-effectiveness (CE) study, the unknown parameters are considered as random variables. A crucial question is what probabilistic distribution is suitable for synthesizing the available information (mainly data from clinical trials) about these parameters. In this context, the important role of Bayesian methodology has been recognized, where the parameters are of a random nature. We explore, in the context of CE analyses, how formal objective Bayesian methods can be implemented. We fully illustrate the methodology using two CE problems that frequently appear in the CE literature. The results are compared with those obtained with other popular approaches to PSA. We find that the discrepancies can be quite marked, specially when the number of patients enrolled in the simulated cohort under study is large. Finally, we describe in detail the numerical methods that need to be used to obtain the results.
Bayes Theorem , Cost-Benefit Analysis/methods , Markov Chains , Monte Carlo Method , Anti-Inflammatory Agents, Non-Steroidal/adverse effects , Anti-Inflammatory Agents, Non-Steroidal/economics , Anti-Inflammatory Agents, Non-Steroidal/therapeutic use , Decision Support Techniques , Humans , Osteoarthritis/drug therapy , Osteoarthritis/economics
