Background: Cost-effectiveness acceptability curves (CEACs) and the cost-effectiveness acceptability frontier (CEAF) are the recommended graphical representations of uncertainty in a cost-effectiveness analysis (CEA). Nevertheless, many limitations of CEACs and the CEAF have been recognized by others. Expected loss curves (ELCs) overcome these limitations by displaying the expected foregone benefits of choosing one strategy over others, the optimal strategy in expectation, and the value of potential future research all in a single figure. Objectives: To revisit ELCs, illustrate their benefits using a case study, and promote their adoption by providing open-source code. Methods: We used a probabilistic sensitivity analysis of a CEA comparing 6 cerebrospinal fluid biomarker test-and-treat strategies in patients with mild cognitive impairment. We showed how to calculate ELCs for a set of decision alternatives. We used the probabilistic sensitivity analysis of the case study to illustrate the limitations of currently recommended methods for communicating uncertainty and then demonstrated how ELCs can address these issues. Results: ELCs combine the probability that each strategy is not cost-effective on the basis of current information and the expected foregone benefits resulting from choosing that strategy (ie, how much is lost if we recommended a strategy with a higher expected loss). ELCs display how the optimal strategy switches across willingness-to-pay thresholds and enables comparison between different strategies in terms of the expected loss. Conclusions: ELCs provide a more comprehensive representation of uncertainty and overcome current limitations of CEACs and the CEAF. Communication of uncertainty in CEA would benefit from greater adoption of ELCs as a complementary method to CEACs, the CEAF, and the expected value of perfect information.
Bibliographical noteFunding Information:
Financial support for this study was provided in part by a grant from the National Council of Science and Technology of Mexico and a doctoral dissertation fellowship from the Graduate School of the University of Minnesota as part of F. Alarid-Escudero’s doctoral program. E. A. Enns was supported by a grant from the National Institute of Allergy and Infectious Disease of the National Institutes of Health (award number: K25AI118476 ). K. M. Kuntz and F. Alarid-Escudero were supported by a grant from the National Cancer Institute (grant number: U01-CA-199335 ) as part of the Cancer Intervention and Surveillance Modeling Network. H. Jalal was supported by a grant from the National Institutes of Health (grant number: KL2 TR0001856 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies had no role in the design of the study, interpretation of results, or writing of the article. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, and writing and publishing the report.
© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research
- cost-effectiveness analysis
- expected losses
- probabilistic sensitivity analysis
- uncertainty analysis
- value of information analysis