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BACKGROUND: With the EF-14 Trial reporting a 5-year survival rate of 13% for newly diagnosed Glioblastoma (GBM) patients treated with TTFields and maintenance temozolomide (TMZ) chemotherapy, a relevant number of patients will become long-term survivors. The impact on resource utilization from a health-economic standpoint is important for all health care decision-makers and therefore cost-effectiveness of TTFields treatment in first line GBM is assessed from a U.S. health system perspective. METHODS: The incremental cost-effectiveness ratio (ICER) for treatment of newly diagnosed GBM with TTFields and TMZ versus TMZ monotherapy was estimated. We used a 3-state area under the curve model including alive without progression, progressed disease, and death to simulate patient outcomes and a lifetime horizon for survival by integrating the 5-year Kaplan-Meier curves of the EF-14 trial with long-term GBM epidemiology data and U.S. background mortality rates. For the calculation of quality-adjusted life years (QALYs) health utility values reported by Garside et al. were used. Frequency of adverse events (AE) were based on the EF-14 trial. AE and supportive care cost estimates were derived from published literature. We applied a discount of 3% to future survival benefits and costs. Parametrical uncertainty was addressed by one-way and probabilistic sensitivity analyses. RESULTS: We calculated an undiscounted increase in mean survival of 1.8 life years for TTFields plus TMZ versus TMZ alone. After applying the 3% discount rate, the incremental total cost was $188,637. The incremental cost-effectiveness ratio (ICER) was $150,452 per life year gained and $197,336 per QALY gained. CONCLUSIONS: Mean lifetime survival and quality-adjusted survival substantially increases by treatment with TTFields plus TMZ compared to treatment with TMZ alone in newly diagnosed GBM patients. Treatment with TTFields for can be considered cost-effective within the reported range of willingness-to-pay thresholds in the United States based on the results of this analysis.

BACKGROUND: The subgroup of patients age 65 and older showed the the greatest survival benefit from TTFields plus TMZ treatment in the EF-14 trial (hazard ratio 0.51 [CI 0.33–0.77]). This is the first report on the cost-effectiveness of adding TTFields to the maintainance phase of the first line treatment protocol for elderly GBM patients from a U.S. health system perspective. MATERIAL AND METHODS: The endpoint of this analysis was the projected incremental cost-effectiveness ratio (ICER) of adding TTFields to the standard-of-care for newly diagnosed GBM in the maintenance phase of treatment for patients over 65 years of age. Patient outcomes were simulated using a 3-state area under the curve model including alive with stable disease, progressed disease, and death. Survival was modelled over a lifetime horizon by integrating the 5-year survival results for elderly patients reported in the EF-14 trial with long-term GBM epidemiology data and U.S. background mortality rates. Data on patient utilities used to calculate quality-adjusted life years (QALYs) were based on a previous analysis of GBM-specific health-state preferences. Frequency of adverse events associated with TTFields and TMZ were derived from the EF-14 trial for the patients over 65 years. Costs for adverse events and supportive care cost estimates were used according to published literature. Future survival benefits and costs were discounted to present value at a rate of 3%. One-way and probabilistic sensitivity analyses were performed to assess result uncertainty due to parameter variability. RESULTS: Patients treated with TTFields and maintenance TMZ achieved mean lifetime survival of 3.66 years before discounting and 3.04 years after discounting future survival. Patients treated with TMZ alone achieved a mean lifetime survival of 1.32 years without discounting and 1.29 years after discounting future survival. The resulting incremental life years gained were 1.75 years after discounting future survival benefits, andthe incremental quality-adjusted life years (QALYs) were 1.35. The incremental total cost was $192,000. The ICER was $109,500 per LYG and $142,400per QALY gained. The probability of TTFields being cost-effective was 85% at a willingness-to-pay threshold of $200,000 per QALY. CONCLUSION: Treating newly diagnosed GBM patients over 65 years of age with TTFields and TMZ has the potential to increase mean lifetime survival and quality-adjusted survival substantially compared to treatment with TMZ alone. TTFields therapy, even when evaluated at its full list price, demonstrated a high probability of cost-effectiveness at willingness-to-pay thresholds reported in economic literature for the United States. These results indicate that patients over age 65 may not only benefit from TTFields treatment more than other subgroups, but also that their treatment may be more cost-effective.

Objective: To estimate the potential cost-effectiveness of treating newly diagnosed glioblastoma patients with tumor treating fields (TTFields) and temozolomide (TMZ) based on data from the final analysis of the EF-14 Trial. Background: Glioblastoma (GBM) is the most aggressive form of primary brain cancer in adults. The EF-14 Trial in GBM patients reported a 5-year survival rate of 13% for newly diagnosed GBM patients treated with TTFields and maintenance temozolomide (TMZ) chemotherapy. The cost-effectiveness of adding TTFields to the maintainance phase of the first line treatment protocol for GBM patients has not been previously assessed from a U.S. health system perspective. Methods: The primary economic endpoint was the projected incremental cost-effectiveness ratio (ICER) for treatment of newly diagnosed GBM with TTFields and TMZ versus TMZ monotherapy alone during the maintenance phase of treatment. Patient outcomes were simulated using a 3-state area under the curve model including alive without progression, progressed disease, and death. Survival was modelled over a lifetime horizon by integrating the 5-year survival results reported in the EF-14 trial with long-term GBM epidemiology data and U.S. background mortality rates. Data on patient utilities used to calculate quality-adjusted life years (QALYs) were based on a previous analysis of GBM-specific health-state preferences. Costs and frequency of adverse events associated with TTFields and TMZ were estimated from the EF-14 trial. Supportive care cost estimates were derived from published literature. Future survival benefits and costs were discounted to present value at a rate of 3%. One-way and probabilistic sensitivity analyses were performed to assess result uncertainty due to parameter variability. Results: Use of TTFields plus TMZ were projected to increase in undiscounted mean survival of 1.8 life years versus TMZ alone. The resulting life years gained (LYG) and quality-adjusted life years (QALYs) were 1.3 and 1.0, respectively, after discounting. The incremental total cost was $188,870. The ICER was $150,638 per LYG and $198,032 per QALY gained. The probability of TTFields being cost-effective was 70.5% at a willingness-to-pay threshold of $200,000 per QALY. Conclusions: Treating newly diagnosed GBM patients with TTFields and TMZ has the potential to increase mean lifetime survival and quality-adjusted survival substantially compared to treatment with TMZ alone. TTFields therapy, even when evaluated at its full list price, demonstrated a high probability of cost-effectiveness at a willingness to pay threshold of $200,000. Citation Format: Gregory F. Guzauskas, Erqi L. Pollom, Volker W. Stieber, Bruce C. Wang, Lou Garrison. Tumor treating fields treatment for patients with newly diagnosed glioblastoma: A cost-effectiveness analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-257.

Progress in personalized medicine (PM) has been slower than expected for a number of reasons – scientific, regulatory, and economic. The science is more difficult than first hoped, and inadequate regulation may also be a barrier. Furthermore, in most developed countries, pricing and reimbursement systems for personalized medicine drugs and their companion diagnostics rely on rigid, administered pricing rules that do not reward value creation appropriately. How the created value is apportioned among drug manufacturers, diagnostic manufacturers, payers, patients, and providers can encourage or discourage personalized medicine development. Flexible value-based rewards are needed to support the appropriate amount of research and development (R&D) and evidence generation.

Advanced imaging modalities have revolutionized medical practice by improving clinical diagnostic ability, although contributing to rapid growth in aggregate spending. Economic characteristics of diagnostic imaging – such as economies of scale for high-cost equipment, informational asymmetry between providers and patients, and the potential for moral hazard – create challenges to efficient use. Economic evaluations show the potential value of appropriate imaging in improving diagnostic accuracy and health outcomes and in reducing uncertainty. But high and variable utilization rates suggest significant overuse. Across-the-board cuts and other utilization controls have curbed spending growth, but the extent of inefficiency – both static and dynamic – remains unclear.

BackgroundPersonalized 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.ObjectivesIn 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.MethodsFive 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.ResultsThese 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.ConclusionsKey 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.

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.

Background Giant cell arteritis (GCA), a chronic vasculitis commonly presenting with headache due to involvement of the temporal arteries, affects approximately 230,000 individuals in the United States (US). However, limited data exist on the health care resource utilization and costs that are attributable to GCA. Objectives The objective of this study was to estimate the cost of illness in patients with GCA in the US. Methods A retrospective cohort of patients with incident GCA and five matched controls was identified from a large US claims database between January 1st 2008 and December 31st 2011. GCA patient status was defined by a diagnosis of GCA (ICD-9 446.5), no GCA diagnosis in the 12-months prior, and age greater or equal to 50 years at the time of diagnosis. To create the control group of matched patients without GCA, patients without GCA were randomly assigned an index event month within a calendar year. GCA patients and controls were matched on age, gender, U.S. region, index year of diagnosis, and index month of diagnosis. Only individuals continuously enrolled for 12 months before and 12 months after the index GCA diagnosis (for GCA patients) or the randomly-assigned index date (for controls) were included. One-year costs of healthcare (pharmacy, outpatient, inpatient, and total) among GCA patients and controls were compared, adjusting for age, gender, Charlson Comorbidity Index (CCI), Chronic Condition Count (CCC), U.S. region, and health plan type (HMO vs. other) using generalized linear models. A log link and gamma family was used to model costs, and recycled prediction to calculate cost differences. Results A cohort of 11,245 GCA patients and 56,230 controls was identified. The mean age of the cohort was 70 years and 71% were females. Mean CCI was 1.6 for GCA patients and 0.8 for controls. Mean CCC was 11.0 for GCA patients and 4.9 for controls. When compared to control patients, GCA patients had higher mean one-year unadjusted pharmacy costs ($3,200, SD $5,700 vs. $2,860, SD $5,300), outpatient costs ($15,000; SD 28,600 vs. $5,860, SD 20,900), inpatient costs ($8,100, SD $29,800 vs. $2,830, SD $14,800), and total costs ($26,400, SD $48,500 vs. $11,500, SD $29,200). After multivariate adjustment, mean one-year cost for GCA patients were lower for pharmacy costs ($-815, 95% CI: $-990–$-640) but higher for outpatient costs ($3,780; 95% CI: $3,350–$4,200) and inpatient costs ($1,500; 95% CI: $1,150–$4,200). Mean adjusted one-year total costs were also higher for GCA patients compared to controls ($4,800; 95% CI: $4,090–$5,520). Conclusions Patients with GCA experience increased healthcare costs compared to patients without GCA after adjusting for covariates related to health care utilization and costs. Our results are the first to inform researchers, clinicians, and policymakers on the cost burden of GCA, estimated to be approximately $1 billion annually in the US. The results may provide guidance for future research and resource allocation decisions. References Lawrence, R.C., et al., Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. Arthritis Rheum, 2008. 58(1): p. 26-35. Acknowledgements The study was funded through an unrestricted research grant to the University of Washington from Genentech Inc. Disclosure of Interest J. Babigumira Grant/research support from: Genentech Inc, M. Li Grant/research support from: Genentech Inc, D. Boudreau Grant/research support from: Genentech Inc, J. Best Employee of: Genentech Inc, L. Garrison Grant/research support from: Genentech Inc