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Pricing methods in outcome-based contracting: δ6: adherence-based pricing.

Authors
  • Alkhatib, Nimer S1, 2
  • Slack, Marion1, 3
  • Bhattacharjee, Sandipan1, 3
  • Erstad, Brian1, 3
  • Ramos, Kenneth4
  • McBride, Ali3, 5, 6
  • Abraham, Ivo1, 3, 6, 7, 8
  • 1 Center for Health Outcomes and PharmacoEconomic Research, College of Pharmacy, University of Arizona, Tucson, AZ, USA.
  • 2 College of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan. , (Jordan)
  • 3 Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA.
  • 4 Institute of BioSciences and Technology, Texas A&M University, Houston, TX, USA.
  • 5 Banner University Medical Center, Tucson, AZ, USA.
  • 6 University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA.
  • 7 Department of Family and Community Medicine, College of Medicine, University of Arizona, Tucson, AZ, USA.
  • 8 Matrix45, Tucson, AZ, USA.
Type
Published Article
Journal
Journal of medical economics
Publication Date
Sep 10, 2020
Pages
1–10
Identifiers
DOI: 10.1080/13696998.2020.1815030
PMID: 32845209
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The sixth dimension (δ6) estimates prices on the basis of adherence to the prescribed regimen, whereby manufacturers provide payers with adherence-enhancing programs and whereby payers implement these programs and provide adherence data to the manufacturer. We describe this dimension's methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. We propose two paybacks based on adherence: in-advance (based on clinical trial data) and in-arrear (based on real-world data). The risk of efficacy failure pricing dimension utilizes a 7-step method: 1) defining efficacy endpoints; 2) extracting data; 3) predicting models; 4) estimating in-advance and in-arrear paybacks; 5) suggesting ranges for in-advance and in-arrear paybacks; 6) adjusting for medical inflation; and 7) performing Monte Carlo Simulation (MCS) to estimate the DSPAdherence. A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-year (2019-2021). The 2018 wholesale acquisition cost (WAC) for a 30-day prescription was used and inflated as needed. Herein, the DSPAdherence is estimated exclusively in terms of in-advance payback because real-world data about osimertinib are not yet available and thus the in-arrear payback cannot yet be estimated. For the 1-year contract, the average price for osimertinib was $13,798 (SD=$1,265) and the DSPAdherence was $13,785 (or -5.69% of the 2018 WAC) for a 30-day prescription. For the 2-year contract, the average price was $12,555 (SD=$2,847) and the DSPAdherence was $12,582 (or -13.92% of the 2018 WAC). We demonstrated that adherence-based pricing methods can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting. The proof-of-concept exercise needs to be expanded with the in-arrear pricing method based on real world data to be secured.

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