A methodological guide for implementing and interpreting results of probabilistic analysis.

Abstract

Introduction: Probabilistic analysis, also referred to as probabilistic sensitivity analysis (PSA), is used extensively in cost-effectiveness evaluations of health technologies. We present methodological guidance for implementing probabilistic analysis and interpreting its results for policy and decision-making.

Methods: We review the methodological issues related to common practices in probabilistic analysis, explore aspects that are currently not widely addressed in the health economics literature, and provide an overview of recent methodological developments.

Results: We use examples to highlight the advantages and disadvantages of common tools used for presenting probabilistic analysis results, including the cost-effectiveness acceptability curve (CEAC), cost-effectiveness acceptability frontier (CEAF), and value of information (VOI) analysis. We raise and address issues related to using Monte Carlo standard error to determine the number of iterations required, the implications of large uncertainty, and the credibility and meaningfulness of small differences in quality-adjusted life-years (QALYs). We then discuss evolving methods in probabilistic analysis, cautious uses of probabilistic analysis, and factors impacting parameter uncertainty.

Conclusions: A deeper understanding of probabilistic analysis methods enables health economists and decision-makers to more effectively address and interpret parameter uncertainty in health economic evaluations, which is essential for making informed policy decisions.