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      Cost Effectiveness of Pembrolizumab vs. Standard-of-Care Chemotherapy as First-Line Treatment for Metastatic NSCLC that Expresses High Levels of PD-L1 in the United States

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          Abstract

          Objectives

          Our objectives were to evaluate the cost effectiveness of pembrolizumab compared with standard-of-care (SoC) platinum-based chemotherapy as first-line treatment in patients with metastatic non-small-cell lung cancer (NSCLC) that expresses high levels of programmed death ligand-1 (PD-L1) [tumour proportion score (TPS) ≥50%], from a US third-party public healthcare payer perspective.

          Methods

          We conducted a partitioned-survival model with a cycle length of 1 week and a base-case time horizon of 20 years. Parametric models were fitted to Kaplan–Meier estimates of time on treatment, progression-free survival and overall survival from the KEYNOTE-024 randomized clinical trial (patients aged ≥18 years with stage IV NSCLC, TPS ≥50%, without epidermal growth factor receptor ( EGFR)-activating mutations or anaplastic lymphoma kinase ( ALK) translocations who received no prior systemic chemotherapy) and validated with long-term registry data. Quality-adjusted life-years (QALYs) were calculated based on EuroQoL-5 Dimensions (EQ-5D) utility data collected in the trial. Costs ($US, year 2016 values) for drug acquisition/administration, adverse events and clinical management were included. Costs and outcomes were discounted at 3% per year. A series of deterministic and probabilistic sensitivity analyses were performed to test the robustness of the results.

          Results

          In the base-case scenario, pembrolizumab resulted in an expected gain of 1.31 life-years (LYs) and 1.05 QALYs and an incremental cost of $US102,439 compared with SoC. The incremental cost per QALY gain was $US97,621/QALY and the incremental cost per LY gain was $US78,344/LY.

          Conclusions

          Pembrolizumab is projected to be a cost-effective option compared with SoC platinum-based chemotherapy as first-line treatment in adults with metastatic NSCLC expressing high levels of PD-L1.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s40273-017-0527-z) contains supplementary material, which is available to authorized users.

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          Most cited references18

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          Tests of Equality Between Sets of Coefficients in Two Linear Regressions

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            US valuation of the EQ-5D health states: development and testing of the D1 valuation model.

            The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights. A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design. Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude. The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US.
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              Cost of care for elderly cancer patients in the United States.

              Timely estimates of the costs of care for cancer patients are an important element in the formulation of national cancer programs and policies. We estimated net costs of care for elderly cancer patients in the United States for the 18 most prevalent cancers and for all other tumor sites combined. We used Surveillance, Epidemiology, and End Results-Medicare files to identify 718,907 cancer patients and 1,623,651 noncancer control subjects. Within each tumor site, noncancer control subjects were matched to patients by sex, age group, geographic location, and phase of care (ie, initial, continuing, and last year of life). Costs of care were estimated for each phase by use of Medicare claims data from January 1, 1999, through December 31, 2003. Per-patient net costs of care were applied to the 5-year survival of cancer patients by phase of care to estimate 5-year costs of care and extrapolated to the elderly US Medicare population diagnosed with cancer in 2004. Across tumor sites, mean net costs of care were highest in the initial and last year of life phases of care and lowest in the continuing phase. Mean 5-year net costs varied widely, from less than $20,000 for patients with breast cancer or melanoma of the skin to more than $40,000 for patients with brain or other nervous system, esophageal, gastric, or ovarian cancers or lymphoma. For elderly cancer patients diagnosed in 2004, aggregate 5-year net costs of care to Medicare were estimated to be approximately $21.1 billion. Costs to Medicare were highest for lung, colorectal, and prostate cancers, reflecting underlying incidence, stage distribution at diagnosis, survival, and phase-specific costs for these tumor sites. The costs of cancer care to Medicare are substantial and vary by tumor site, phase of care, stage at diagnosis, and survival.
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                Author and article information

                Contributors
                Min_Huang@merck.com
                Lou.Yanyan@mayo.edu
                James_Pellissier@merck.com
                Thomas_Burke2@merck.com
                Xiaoqing.Liu@merck.com
                Ruifeng_Xu@merck.com
                velchev@ccf.org
                Journal
                Pharmacoeconomics
                Pharmacoeconomics
                Pharmacoeconomics
                Springer International Publishing (Cham )
                1170-7690
                1179-2027
                15 June 2017
                15 June 2017
                2017
                : 35
                : 8
                : 831-844
                Affiliations
                [1 ]ISNI 0000 0001 2260 0793, GRID grid.417993.1, Centre for Observational and Real-world Evidence, , Merck & Co., Inc., ; 2000 Galloping Hill Road, Kenilworth, NJ 07033 USA
                [2 ]ISNI 0000 0004 0443 9942, GRID grid.417467.7, Department of Hematology and Oncology, , Mayo Clinic, ; 4500 San Pablo Road, Jacksonville, FL 27710 USA
                [3 ]ISNI 0000 0001 0675 4725, GRID grid.239578.2, Solid Tumor Oncology, , Cleveland Clinic, ; 9500 Euclid Ave, Mail Code R35, Cleveland, OH 44195 USA
                Author information
                http://orcid.org/0000-0003-1960-4552
                Article
                527
                10.1007/s40273-017-0527-z
                5548835
                28620848
                e16a515b-8f18-4de0-85a3-a0595e4db1d8
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Funding
                Funded by: Merck & Co. Inc.
                Categories
                Original Research Article
                Custom metadata
                © Springer International Publishing AG 2017

                Economics of health & social care
                Economics of health & social care

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