A Cost-Benefit Analysis of Using Evidence of Effectiveness in Terms of Progression Free Survival in Making Reimbursement Decisions on New Cancer Therapies

Warren Stevens 1 , Tomas Philipson 2 , Yanyu Wu 3 , Connie Chen 4 ,  and Darius Lakdawalla 5
  • 1 Precision Health Economics, 11100 Santa Monica Blvd Suite 500, Los Angeles, CA 90025, USA
  • 2 University of Chicago – The Harris School, 1155 E. 60th St., Suite 112, Chicago, IL 60637, USA
  • 3 Precision Health Economics, 800 Boylston Street, 16th Fl, Boston, MA 02199, USA
  • 4 Pfizer, 235 East 42nd Street, New York, NY 10017, USA
  • 5 University of Southern California, University Gateway 100D, Los Angeles, CA 90089, USA
Warren Stevens, Tomas Philipson, Yanyu Wu, Connie Chen and Darius Lakdawalla

Abstract

Payers increasingly require evidence of a statistically significant difference in overall survival (OS) for reimbursement of new cancer therapies. At the same time, it becomes increasingly costly to design clinical trials that measure OS endpoints instead of progression-free survival (PFS) endpoints. While PFS is often an imperfect proxy for OS effects, it is also faster and cheaper to measure accurately. This study develops a general cost-benefit framework that quantifies the competing trade-offs of the use of PFS versus that of OS in oncology reimbursement. We then apply this general framework to the illustrative case of metastatic renal cell carcinoma (mRCC). In the particular case of mRCC, the framework demonstrates that the net benefit to society from basing reimbursement decisions on PFS endpoints could be between $271 and $1271 million in the United States, or between €171 and €1128 million in Europe. In longevity terms, waiting for OS data in this case would result in a net loss of 3549–14,557 life-years among US patients, or 6785–27,993 life-years for European patients. While more stringent standards for medical evidence improve accuracy, they also impose countervailing costs on patients in terms of foregone health gains. These costs must be weighed against the benefits of greater accuracy. The magnitudes of the costs and benefits may vary across tumor types and need to be quantified systematically.

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