Key data elements for use in cost-utility modeling of biological treatments for rheumatoid arthritis. (May 2015)

• Record Type: Journal Article
• Authors: Ganz, Michael L.; Hansen, Brian Bekker; Valencia, Xavier; Strandberg-Larsen, Martin
• Is Part Of: Journal of medical economics. Volume 18:Number 5(2015)
• Page Start: 366
• Page End: 375
• Publication Date: 2015-05
• Subjects: Medical care -- Cost control -- Periodicals; Medical economics -- Periodicals; 362.10941
• DOI: 10.3111/13696998.2014.1001848
• Languages: English
• ISSNs: 1369-6998

Abstract

<abstract> <title>Abstract</title> <sec id="ss1"> <title>Objectives:</title> <p>Economic evaluation is becoming more common and important as new biologic therapies for rheumatoid arthritis (RA) are developed. While much has been published about how to design cost-utility models for RA to conduct these evaluations, less has been written about the sources of data populating those models. The goal is to review the literature and to provide recommendations for future data collection efforts.</p> </sec> <sec id="ss2"> <title>Methods:</title> <p>This study reviewed RA cost-utility models published between January 2006 and February 2014 focusing on five key sources of data (health-related quality-of-life and utility, clinical outcomes, disease progression, course of treatment, and healthcare resource use and costs). It provided recommendations for collecting the appropriate data during clinical and other studies to support modeling of biologic treatments for RA.</p> </sec> <sec id="ss3"> <title>Results:</title> <p>Twenty-four publications met the selection criteria. Almost all used two steps to convert clinical outcomes data to utilities rather than more direct methods; most did not use clinical outcomes measures that captured absolute levels of disease activity and physical functioning; one-third of them, in contrast with clinical reality, assumed zero disease progression for biologic-treated patients; little more than half evaluated courses of treatment reflecting guideline-based or actual clinical care; and healthcare resource use and cost data were often incomplete.</p> </sec> <sec id="ss4"> <title>Conclusions:</title> <p>Based on these findings, it is recommended that future studies collect clinical outcomes and health-related quality-of-life data using appropriate instruments that can convert directly to utilities; collect data on actual disease progression; be designed to capture real-world courses of treatment; and collect detailed data on a wide range of healthcare resources and costs.</p> </sec> </abstract>