CC BY-NC-ND 4.0 · Semin Speech Lang 2022; 43(03): 244-254
DOI: 10.1055/s-0042-1750347
Review Article

Analyzing a Cost-Effectiveness Dataset: A Speech and Language Example for Clinicians

Jeffrey S. Hoch
1   Division of Health Policy and Management, Department of Public Health Sciences, University of California, Davis, California
2   Center for Healthcare Policy and Research, University of California, Davis, California
Sarah C. Haynes
3   Department of Pediatrics, University of California, Davis, California
4   Center for Health and Technology, UC Davis Health, Sacramento, California
Shannon M. Hearney
1   Division of Health Policy and Management, Department of Public Health Sciences, University of California, Davis, California
Carolyn S. Dewa
1   Division of Health Policy and Management, Department of Public Health Sciences, University of California, Davis, California
5   Department of Psychiatry and Behavioral Sciences, University of California, Davis, California
› Author Affiliations


Cost-effectiveness analysis, the most common type of economic evaluation, estimates a new option's additional outcome in relation to its extra costs. This is crucial to study within the clinical setting because funding for new treatments and interventions is often linked to whether there is evidence showing they are a good use of resources. This article describes how to analyze a cost-effectiveness dataset using the framework of a net benefit regression. The process of creating estimates and characterizing uncertainty is demonstrated using a hypothetical dataset. The results are explained and illustrated using graphs commonly employed in cost-effectiveness analyses. We conclude with a call to action for researchers to do more person-level cost-effectiveness analysis to produce evidence of the value of new treatments and interventions. Researchers can utilize cost-effectiveness analysis to compare new and existing treatment mechanisms.

Publication History

Article published online:
20 July 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

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