Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group.

BACKGROUND: Personalized medicine technologies can improve individual health by delivering the right dose of the right drug to the right patient at the right time but create challenges in deciding which technologies offer sufficient value to justify widespread diffusion. Personalized medicine technologies, however, do not neatly fit into existing health technology assessment and reimbursement processes. OBJECTIVES: In this article, the Personalized Medicine Special Interest Group of the International Society for Pharmacoeconomics and Outcomes Research evaluated key development and reimbursement considerations from the payer and manufacturer perspectives. METHODS: Five key areas in which health economics and outcomes research best practices could be developed to improve value assessment, reimbursement, and patient access decisions for personalized medicine have been identified. RESULTS: These areas are as follows: 1 research prioritization and early value assessment, 2 best practices for clinical evidence development, 3 best practices for health economic assessment, 4 addressing health technology assessment challenges, and 5 new incentive and reimbursement approaches for personalized medicine. CONCLUSIONS: Key gaps in health economics and outcomes research best practices, decision standards, and value assessment processes are also discussed, along with next steps for evolving health economics and outcomes research practices in personalized medicine.

Economic evaluation of everolimus versus sorafenib for the treatment of metastatic renal cell carcinoma after failure of first-line sunitinib.

BACKGROUND: A recent indirect comparison study showed that sunitinib-refractory metastatic renal cell carcinoma (mRCC) patients treated with everolimus are expected to have improved overall survival outcomes compared to patients treated with sorafenib. This analysis examines the likely cost-effectiveness of everolimus versus sorafenib in this setting from a US payer perspective. METHODS: A Markov model was developed to simulate a cohort of sunitinib-refractory mRCC patients and to estimate the cost per incremental life-years gained (LYG) and quality-adjusted life-years (QALYs) gained. Markov states included are stable disease without adverse events, stable disease with adverse events, disease progression, and death. Transition probabilities were estimated using a subset of the RECORD-1 patient population receiving everolimus after sunitinib, and a comparable population receiving sorafenib in a single-arm phase II study. Costs of antitumor therapies were based on wholesale acquisition cost. Health state costs accounted for physician visits, tests, adverse events, postprogression therapy, and end-of-life care. The model extrapolated beyond the trial time horizon for up to 6 years based on published trial data. Deterministic and probabilistic sensitivity analyses were conducted. RESULTS: The estimated gain over sorafenib treatment was 1.273 LYs (0.916 QALYs) at an incremental cost of $81,643. The deterministic analysis resulted in an incremental cost-effectiveness ratio (ICER) of $64,155/LYG ($89,160/QALY). The probabilistic sensitivity analysis demonstrated that results were highly consistent across simulations. CONCLUSIONS: As the ICER fell within the cost per QALY range for many other widely used oncology medicines, everolimus is projected to be a cost-effective treatment relative to sorafenib for sunitinib-refractory mRCC.