Abstract
Objectives
The primary objective of this study was to predict healthcare cost trajectories for patients with newly diagnosed acute myeloid leukemia (AML) receiving allogeneic hematopoietic cell transplantation (alloHCT), as a function of days since chemotherapy initiation, days relative to alloHCT, and days before death or last date of insurance eligibility (LDE). An exploratory objective examined patients with AML receiving chemotherapy only.
Methods
We used Optum’s de-identified Clinformatics® Data Mart Database to construct cumulative cost trajectories from chemotherapy initiation to death or LDE (through 31 December 2014) for US patients aged 20–74 years diagnosed between 1 March 2004 and 31 December 2013 (n = 187 alloHCT; n = 253 chemotherapy only). We used generalized additive modeling (GAM) to predict expected trajectories and bootstrapped confidence intervals (CIs) at user-specified intervals conditional on dates of alloHCT and death or LDE relative to chemotherapy initiation.
Results
Expected costs (in 2017 values) for a hypothetical patient receiving alloHCT 60 days after chemotherapy initiation and followed for 5 years were $US572,000 (95% CI 517,000–633,000); $US119,000 (95% CI 51,000–192,000); $US102,000 (95% CI 0–285,000); $US79,000 (95% CI 0–233,000), for years 1–4, respectively, and either $US494,000 (95% CI 212,000–799,000) or $US108,000 (95% CI 0–230,000) in year 5, whether the patient died or was lost to follow-up on day 1825, respectively.
Conclusions
Rates of cost accrual varied over time since chemotherapy initiation, with accelerations around the time of alloHCT and death. GAM is a potentially useful approach for imputing longitudinal costs relative to treatment initiation and one or more intercurrent, clinical, or terminal events in randomized controlled trials or registries with unrecorded costs or for dynamic decision–analytic models.
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Data Availability Statement
Optum’s de-identified Clinformatics® Data Mart Database is commercially available. According to the terms of the data purchase agreement, the dataset used in this study cannot be shared. Analysis code is available at: https://github.com/djvanness/pharmacoecon_GAM_AML.
Notes
Corporate members.
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Acknowledgements
The authors thank Kellene Bergen for her contributions to data interpretation and members of the CIBMTR® (Center for International Blood and Marrow Transplant Research®) statistical review panel for their helpful comments and suggestions.
Disclosure
CIBMTR® (Center for International Blood and Marrow Transplant Research®) is a research collaboration between the National Marrow Donor Program®/Be The Match® and the Medical College of Wisconsin. CIBMTR® is supported primarily by Public Health Service Grant/cooperative agreement 5U24CA076518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI), and the National Institute of Allergy and Infectious Diseases (NIAID); Grant/cooperative agreement 1U24HL138660 from NHLBI and NCI; contract HHSH250201700006C with the Health Resources and Services Administration/Department of Health and Human Service (HRSA/DHHS); Grants N00014-17-1-2388, N00014-17-1-2850, and N00014-18-1-2045 from the Office of Naval Research; and Grants from Adaptive Biotechnologies, Amgen, Inc.,Footnote 1 anonymous donation to the Medical College of Wisconsin, Astellas Pharma US, Atara Biotherapeutics, Inc., Be the Match Foundation, bluebird bio, Inc.,1 Bristol Myers Squibb Oncology,1 Celgene Corporation,1 Chimerix, Inc.,1 CytoSen Therapeutics, Inc.,1 Fred Hutchinson Cancer Research Center, Gamida Cell Ltd., Gilead Sciences, Inc., HistoGenetics, Inc., Immucor, Incyte Corporation,1 Janssen Scientific Affairs, LLC, Jazz Pharmaceuticals, Inc.,1 Karius, Inc., Karyopharm Therapeutics, Inc., Kite Pharma,1 Inc., Medac, GmbH, Mediware,1 The Medical College of Wisconsin, Merck & Co., Inc.,1 Mesoblast,1 MesoScale Diagnostics, Inc., Millennium, the Takeda Oncology Co., Miltenyi Biotec, Inc.,1 Mundipharma EDO, National Marrow Donor Program, Novartis Pharmaceuticals Corporation, PCORI, Pfizer, Inc.,1 Pharmacyclics, LLC,1 PIRCHE AG, Sanofi Genzyme,1 Seattle Genetics,1 Shire, Spectrum Pharmaceuticals, Inc., St. Baldrick’s Foundation, Swedish Orphan Biovitrum, Inc., Takeda Oncology,1 and University of Minnesota. The views expressed in this article do not reflect the official policy or position of the National Institute of Health, the Department of the Navy, the Department of Defense, Health Resources and Services Administration (HRSA) or any other agency of the US Government. The Health Services Research program is supported in part by Health Resources and Services Administration contract no. HHSH234200637018C. The views expressed in this article do not reflect the official policy or position of the Health Resources and Services Administration or the National Marrow Donor Program®/Be The Match®.
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Lih-Wen Mau, Jaime M. Preussler, Linda J. Burns, Susan Leppke, Christa L. Meyer, and Tatenda Mupfudze report employment by the National Marrow Donor Program®/Be The Match® in connection with the work; Navneet S. Majhail and Navneet S. Majhail report consulting income from Anthem Inc., Mallinckrodt, Nkarta, and Inctye for unrelated work; Wael Saber and Patricia Steinert report employment by the Center for International Blood and Marrow Transplant Research; David J. Vanness reports compensation from the National Marrow Donor Program®/Be The Match® for this work and consulting income from Novartis, Merck, CHEORS (Complete Health Economics Outcomes and Research Solutions), Medical Decision Modeling Inc., and Bristol-Myers Squibb for unrelated work.
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Mau, LW., Preussler, J.M., Burns, L.J. et al. Healthcare Costs of Treating Privately Insured Patients with Acute Myeloid Leukemia in the United States from 2004 to 2014: A Generalized Additive Modeling Approach. PharmacoEconomics 38, 515–526 (2020). https://doi.org/10.1007/s40273-020-00891-w
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DOI: https://doi.org/10.1007/s40273-020-00891-w