Objective: To describe the application of a probabilistic cost-effectiveness analysis to nasal continuous positive airway passage (nCPAP) treatment of obstructive sleep apnea syndrome (OSAS).
Material and methods: The probabilistic model was constructed from a discrete Markov model. This probabilistic approach is characterized by the introduction of variables as probability distributions. The model performed 2,000 Monte Carlo simulations, and incremental costs and effectiveness were calculated in each. The results were analyzed through the cost-effectiveness plane, the acceptability curve, the net benefit rule, and the expected value of perfect information (EVPI).
Results: The mean cost-effectiveness ratio for nCPAP treatment was 5,480 Euro/QALY (quality-adjusted life year). Using an acceptability threshold of 30,000 Euro/QALY, the probabilistic analysis showed that nCPAP was the optimal treatment in 98.5% of the simulations. The EVPI showed that the parameter causing greatest uncertainty in the final results was the quality of life gain through nCPAP treatment.
Conclusions: The results of our probabilistic analysis are endorsed by previous deterministic studies confirming that nCPAP treatment of OSAS is the most cost-effective strategy. An additional advantage of probabilistic analysis is that it allows uncertainty to be quantified; in the present case the probability of making the wrong decision was below 5%. Furthermore, this study reveals that to reduce uncertainty, research should center on improving information on quality of life.