Cost-effectiveness of Novel Treatment Sequences for Transplant-Ineligible Patients With Multiple Myeloma | Hematology | JAMA Network Open | JAMA Network
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Figure 1.  Time to Event and Costs per Regimen for Line 1, 2, and 3 Treatment Sequences
Time to Event and Costs per Regimen for Line 1, 2, and 3 Treatment Sequences

BorDex indicates bortezomib-dexamethasone; CarDex, carfilzomib-dexamethasone; CarLenDex, carfilzomib-lenalidomide-dexamethasone; DaraBorDex, daratumumab-bortezomib-dexamethasone; DaraLenDex, daratumumab-lenalidomide-dexamethasone; DaraVMP, daratumumab-bortezomib-melphalan-prednisone; EloLenDex, elotuzumab-lenalidomide-dexamethasone; LenDex, lenalidomide-dexamethasone; PanoBorDex, panobinostat-lenalidomide-dexamethasone; PomBorDex, pomalidomide-bortezomib-dexamethasone; PomDex, pomalidomide-dexamethasone; VMP, bortezomib-melphalan-prednisone; VRD, bortezomib-lenalidomide-dexamethasone; VMPT-VT, bortezomib-melphalan-prednisone-thalidomide-maintenance bortezomib-thalidomide.

Figure 2.  Effects and Total Costs per Treatment Sequence
Effects and Total Costs per Treatment Sequence

BorDex indicates bortezomib-dexamethasone; CarDex, carfilzomib-dexamethasone; CarLenDex, carfilzomib-lenalidomide-dexamethasone; DaraBorDex, daratumumab-bortezomib-dexamethasone; DaraLenDex, daratumumab-lenalidomide-dexamethasone; DaraVMP, daratumumab-bortezomib-melphalan-prednisone; EloLenDex, elotuzumab-lenalidomide-dexamethasone; LenDex, lenalidomide-dexamethasone; PanoBorDex, panobinostat-lenalidomide-dexamethasone; PomBorDex, pomalidomide-bortezomib-dexamethasone; PomDex, pomalidomide-dexamethasone; VMP, bortezomib-melphalan-prednisone; VRD, bortezomib-lenalidomide-dexamethasone; VMPT-VT, bortezomib-melphalan-prednisone-thalidomide-maintenance bortezomib-thalidomide.

Figure 3.  Incremental Overall Survival and Costs Compared With Bortezomib-Melphalan-Prednisone–Lenalidomide-Dexamethasone–Pomalidomide-Dexamethasone (VMP-LenDex-PomDex)
Incremental Overall Survival and Costs Compared With Bortezomib-Melphalan-Prednisone–Lenalidomide-Dexamethasone–Pomalidomide-Dexamethasone (VMP-LenDex-PomDex)

BorDex indicates bortezomib-dexamethasone; CarDex, carfilzomib-dexamethasone; CarLenDex, carfilzomib-lenalidomide-dexamethasone; DaraBorDex, daratumumab-bortezomib-dexamethasone; DaraLenDex, daratumumab-lenalidomide-dexamethasone; DaraVMP, daratumumab-bortezomib-melphalan-prednisone; EloLenDex, elotuzumab-lenalidomide-dexamethasone; LenDex, lenalidomide-dexamethasone; PanoBorDex, panobinostat-lenalidomide-dexamethasone; PomBorDex, pomalidomide-bortezomib-dexamethasone; PomDex, pomalidomide-dexamethasone; VMP, bortezomib-melphalan-prednisone; VRD, bortezomib-lenalidomide-dexamethasone; VMPT-VT, bortezomib-melphalan-prednisone-thalidomide-maintenance bortezomib-thalidomide.

Table.  Outcomes of Treatment Sequences and Incremental Differences Compared With the Reference Case (Discounted)a
Outcomes of Treatment Sequences and Incremental Differences Compared With the Reference Case (Discounted)a
1.
European Cancer Information System. Estimates of cancer incidence and mortality in 2020, for all cancer sites, estimated incidence by cancer—summary: multiple myeloma. Accessed January 27, 2021. https://ecis.jrc.ec.europa.eu/explorer.php
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National Cancer Institute; Surveillance, Epidemiology, and End Results Program (SEER): Cancer stat facts: myeloma. Accessed January 26, 2021. https://seer.cancer.gov/statfacts/html/mulmy.html
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Blommestein  HM, Verelst  SG, de Groot  S, Huijgens  PC, Sonneveld  P, Uyl-de Groot  CA.  A cost-effectiveness analysis of real-world treatment for elderly patients with multiple myeloma using a full disease model.   Eur J Haematol. 2016;96(2):198-208. doi:10.1111/ejh.12571PubMedGoogle ScholarCrossref
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Raab  MS, Cavo  M, Delforge  M,  et al.  Multiple myeloma: practice patterns across Europe.   Br J Haematol. 2016;175(1):66-76. doi:10.1111/bjh.14193PubMedGoogle ScholarCrossref
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Yong  K, Delforge  M, Driessen  C,  et al.  Multiple myeloma: patient outcomes in real-world practice.   Br J Haematol. 2016;175(2):252-264. doi:10.1111/bjh.14213PubMedGoogle ScholarCrossref
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Stewart  AK, Rajkumar  SV, Dimopoulos  MA,  et al; ASPIRE Investigators.  Carfilzomib, lenalidomide, and dexamethasone for relapsed multiple myeloma.   N Engl J Med. 2015;372(2):142-152. doi:10.1056/NEJMoa1411321PubMedGoogle ScholarCrossref
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Moreau  P, Masszi  T, Grzasko  N,  et al; TOURMALINE-MM1 Study Group.  Oral ixazomib, lenalidomide, and dexamethasone for multiple myeloma.   N Engl J Med. 2016;374(17):1621-1634. doi:10.1056/NEJMoa1516282PubMedGoogle ScholarCrossref
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Dimopoulos  MA, Oriol  A, Nahi  H,  et al; POLLUX Investigators.  Daratumumab, lenalidomide, and dexamethasone for multiple myeloma.   N Engl J Med. 2016;375(14):1319-1331. doi:10.1056/NEJMoa1607751PubMedGoogle ScholarCrossref
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Palumbo  A, Chanan-Khan  A, Weisel  K,  et al; CASTOR Investigators.  Daratumumab, bortezomib, and dexamethasone for multiple myeloma.   N Engl J Med. 2016;375(8):754-766. doi:10.1056/NEJMoa1606038PubMedGoogle ScholarCrossref
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Lonial  S, Dimopoulos  M, Palumbo  A,  et al; ELOQUENT-2 Investigators.  Elotuzumab therapy for relapsed or refractory multiple myeloma.   N Engl J Med. 2015;373(7):621-631. doi:10.1056/NEJMoa1505654PubMedGoogle ScholarCrossref
11.
Durie  BGM, Hoering  A, Abidi  MH,  et al.  Bortezomib with lenalidomide and dexamethasone versus lenalidomide and dexamethasone alone in patients with newly diagnosed myeloma without intent for immediate autologous stem-cell transplant (SWOG S0777): a randomised, open-label, phase 3 trial.   Lancet. 2017;389(10068):519-527. doi:10.1016/S0140-6736(16)31594-XPubMedGoogle ScholarCrossref
12.
San-Miguel  JF, Hungria  VT, Yoon  SS,  et al.  Panobinostat plus bortezomib and dexamethasone versus placebo plus bortezomib and dexamethasone in patients with relapsed or relapsed and refractory multiple myeloma: a multicentre, randomised, double-blind phase 3 trial.   Lancet Oncol. 2014;15(11):1195-1206. doi:10.1016/S1470-2045(14)70440-1PubMedGoogle ScholarCrossref
13.
Dimopoulos  MA, Moreau  P, Palumbo  A,  et al; ENDEAVOR Investigators.  Carfilzomib and dexamethasone versus bortezomib and dexamethasone for patients with relapsed or refractory multiple myeloma (ENDEAVOR): a randomised, phase 3, open-label, multicentre study.   Lancet Oncol. 2016;17(1):27-38. doi:10.1016/S1470-2045(15)00464-7PubMedGoogle ScholarCrossref
14.
Dimopoulos  MA, Goldschmidt  H, Niesvizky  R,  et al.  Carfilzomib or bortezomib in relapsed or refractory multiple myeloma (ENDEAVOR): an interim overall survival analysis of an open-label, randomised, phase 3 trial.   Lancet Oncol. 2017;18(10):1327-1337. doi:10.1016/S1470-2045(17)30578-8PubMedGoogle ScholarCrossref
15.
Siegel  DS, Dimopoulos  MA, Ludwig  H,  et al.  Improvement in overall survival with carfilzomib, lenalidomide, and dexamethasone in patients with relapsed or refractory multiple myeloma.   J Clin Oncol. 2018;36(8):728-734. doi:10.1200/JCO.2017.76.5032PubMedGoogle ScholarCrossref
16.
Zheng  Y, Pan  F, Sorensen  S.  Modeling treatment sequences in pharmacoeconomic models.   Pharmacoeconomics. 2017;35(1):15-24. doi:10.1007/s40273-016-0455-3PubMedGoogle ScholarCrossref
17.
Roy  A, Kish  JK, Bloudek  L,  et al.  Estimating the costs of therapy in patients with relapsed and/or refractory multiple myeloma: a model framework.   Am Health Drug Benefits. 2015;8(4):204-215.PubMedGoogle Scholar
18.
Borg  S, Nahi  H, Hansson  M, Lee  D, Elvidge  J, Persson  U.  Cost effectiveness of pomalidomide in patients with relapsed and refractory multiple myeloma in Sweden.   Acta Oncol. 2016;55(5):554-560. doi:10.3109/0284186X.2015.1096021PubMedGoogle ScholarCrossref
19.
Carlson  JJ, Guzauskas  GF, Chapman  RH,  et al.  Cost-effectiveness of drugs to treat relapsed/refractory multiple myeloma in the united states.   J Manag Care Spec Pharm. 2018;24(1):29-38. doi:10.18553/jmcp.2018.24.1.29PubMedGoogle Scholar
20.
Jakubowiak  AJ, Campioni  M, Benedict  Á,  et al.  Cost-effectiveness of adding carfilzomib to lenalidomide and dexamethasone in relapsed multiple myeloma from a US perspective.   J Med Econ. 2016;19(11):1061-1074. doi:10.1080/13696998.2016.1194278PubMedGoogle ScholarCrossref
21.
Jakubowiak  AJ, Houisse  I, Májer  I,  et al.  Cost-effectiveness of carfilzomib plus dexamethasone compared with bortezomib plus dexamethasone for patients with relapsed or refractory multiple myeloma in the United States.   Expert Rev Hematol. 2017;10(12):1107-1119. doi:10.1080/17474086.2017.1391088PubMedGoogle ScholarCrossref
22.
Blommestein  HM, Franken  MG, Uyl-de Groot  CA.  A practical guide for using registry data to inform decisions about the cost effectiveness of new cancer drugs: lessons learned from the PHAROS registry.   Pharmacoeconomics. 2015;33(6):551-560. doi:10.1007/s40273-015-0260-4PubMedGoogle ScholarCrossref
23.
Verelst  SGR, Blommestein  HM, De Groot  S,  et al.  Long-term outcomes in patients with multiple myeloma: a retrospective analysis of the Dutch Population-based HAematological Registry for Observational Studies (PHAROS).   Hemasphere. 2018;2(4):e45. doi:10.1097/HS9.0000000000000045PubMedGoogle Scholar
24.
Caro  JJ, Möller  J, Getsios  D.  Discrete event simulation: the preferred technique for health economic evaluations?   Value Health. 2010;13(8):1056-1060. doi:10.1111/j.1524-4733.2010.00775.xPubMedGoogle ScholarCrossref
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van de Poll-Franse  LV, Horevoorts  N, van Eenbergen  M,  et al; Profiles Registry Group.  The Patient Reported Outcomes Following Initial treatment and Long term Evaluation of Survivorship registry: scope, rationale and design of an infrastructure for the study of physical and psychosocial outcomes in cancer survivorship cohorts.   Eur J Cancer. 2011;47(14):2188-2194. doi:10.1016/j.ejca.2011.04.034PubMedGoogle ScholarCrossref
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van Beurden-Tan  CHY, Franken  MG, Blommestein  HM, Uyl-de Groot  CA, Sonneveld  P.  Systematic literature review and network meta-analysis of treatment outcomes in relapsed and/or refractory multiple myeloma.   J Clin Oncol. 2017;35(12):1312-1319. doi:10.1200/JCO.2016.71.1663PubMedGoogle ScholarCrossref
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Blommestein  HM, van Beurden-Tan  CHY, Franken  MG, Uyl-de Groot  CA, Sonneveld  P, Zweegman  S.  Efficacy of first-line treatments for multiple myeloma patients not eligible for stem cell transplantation: a network meta-analysis.   Haematologica. 2019;104(5):1026-1035. doi:10.3324/haematol.2018.206912PubMedGoogle ScholarCrossref
28.
Facon  T, Kumar  S, Plesner  T,  et al; MAIA Trial Investigators.  Daratumumab plus lenalidomide and dexamethasone for untreated myeloma.   N Engl J Med. 2019;380(22):2104-2115. doi:10.1056/NEJMoa1817249PubMedGoogle ScholarCrossref
29.
Mateos  MV, Cavo  M, Blade  J,  et al.  Overall survival with daratumumab, bortezomib, melphalan, and prednisone in newly diagnosed multiple myeloma (ALCYONE): a randomised, open-label, phase 3 trial.   Lancet. 2020;395(10218):132-141. doi:10.1016/S0140-6736(19)32956-3PubMedGoogle ScholarCrossref
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Richardson  PG, Oriol  A, Beksac  M,  et al; OPTIMISMM Trial Investigators.  Pomalidomide, bortezomib, and dexamethasone for patients with relapsed or refractory multiple myeloma previously treated with lenalidomide (OPTIMISMM): a randomised, open-label, phase 3 trial.   Lancet Oncol. 2019;20(6):781-794. doi:10.1016/S1470-2045(19)30152-4PubMedGoogle ScholarCrossref
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Gaultney  JG, Franken  MG, Tan  SS,  et al.  Real-world health care costs of relapsed/refractory multiple myeloma during the era of novel cancer agents.   J Clin Pharm Ther. 2013;38(1):41-47. doi:10.1111/jcpt.12020PubMedGoogle ScholarCrossref
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Pelligra  CG, Parikh  K, Guo  S,  et al.  Cost-effectiveness of pomalidomide, carfilzomib, and daratumumab for the treatment of patients with heavily pretreated relapsed-refractory multiple myeloma in the United States.   Clin Ther. 2017;39(10):1986-2005.e5. doi:10.1016/j.clinthera.2017.08.010PubMedGoogle ScholarCrossref
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    Original Investigation
    Hematology
    March 29, 2021

    Cost-effectiveness of Novel Treatment Sequences for Transplant-Ineligible Patients With Multiple Myeloma

    Author Affiliations
    • 1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, the Netherlands
    • 2Institute of Medical Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, the Netherlands
    • 3Department of Hematology, Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
    • 4Department of Hematology, Radboud University Medical Center, Nijmegen, the Netherlands
    • 5Department of Hematology, Cancer Center Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
    JAMA Netw Open. 2021;4(3):e213497. doi:10.1001/jamanetworkopen.2021.3497
    Key Points

    Question  What is the optimal sequence of treatments for patients with multiple myeloma from the perspective of the patient, physician, and society?

    Findings  This economic evaluation found that sequences starting with daratumumab-bortezomib-melphalan-prednisone (second line: carfilzomib-lenalidomide-dexamethasone or elotuzumab-lenalidomide-dexamethasone) or bortezomib-melphalan-prednisone-thalidomide-maintenance bortezomib-thalidomide (VMPT-VT) (second line: daratumumab-lenalidomide-dexamethasone) had the largest expected overall survival (7.5 years); total costs per patient for these sequences ranged between $786 024 and $1 085 794. The sequence VMPT-VT-carfilzomib-lenalidomide-dexamethasone-panobinostat-bortezomib-dexamethasone had the most favorable cost-effectiveness ratio ($98 585 per life-year gained, and $132 707 per quality-adjusted life-year).

    Meaning  These findings can support clinical decision-making and guideline development, reimbursement decisions, and price negotiations.

    Abstract

    Importance  Although the number of treatments for elderly patients with non–transplant-eligible (NTE) multiple myeloma (MM) has increased substantially, evidence is lacking on the clinical effectiveness and cost-effectiveness of novel treatment sequences.

    Objective  To determine the optimal sequence of treatment for patients with NTE MM from the perspective of the patient, physician, and society.

    Design, Setting, and Participants  Using data from a Dutch observational registry, this economic evaluation combined evidence from network meta-analyses in a patient-level simulation model and modeled time-to-event and types of events from a hospital perspective with a lifetime horizon. Data analysis was performed from June 2019 to September 2020.

    Interventions  Thirty treatment sequences, including up to 3 lines of therapy, were compared with bortezomib-melphalan-prednisone (VMP)–lenalidomide-dexamethasone (LenDex)–pomalidomide-dexamethasone (PomDex).

    Main Outcomes and Measures  The primary outcomes of the model were overall survival (OS), quality-adjusted life-years (QALYs), costs, and cost-effectiveness.

    Results  Sequences starting with daratumumab-VMP (second line: carfilzomib-lenalidomide-dexamethasone or elotuzumab-lenalidomide-dexamethasone) or bortezomib-melphalan-prednisone-thalidomide-maintenance bortezomib-thalidomide (VMPT-VT) (second line: daratumumab-lenalidomide-dexamethasone) had the largest expected OS (7.5 years), which is 3.5 additional life-years compared with VMP-LenDex-PomDex. Total costs per patient for these sequences ranged between $786 024 and $1 085 794. The sequence VMPT-VT-carfilzomib-lenalidomide-dexamethasone–panobinostat-bortezomib-dexamethasone had the most favorable cost-effectiveness ratio ($98 585 per life-year gained and $132 707 per QALY gained vs VMP-LenDex-PomDex).

    Conclusions and Relevance  These findings suggest that sequences including novel treatments were highly effective, but the cost-effectiveness ratios were above currently accepted willingness-to-pay thresholds. Treating MM with novel agents necessitates either a large increase in budget or a substantial reduction of drug costs by price negotiations, and these findings can support these reimbursement decisions and price negotiations.

    Introduction

    Multiple myeloma (MM) is the second most common type of blood cancer, with 50 918 patients with new diagnoses annually in Europe1 and 32 270 such patients annually in North America.2 Like many cancers, it is incurable, and treatment aims at prolonging the time to disease progression to control disease symptoms and increase overall survival (OS). Eventually, the disease will progress (again), and patients, therefore, often receive several lines of treatment.3-5

    In the past 12 years, the number of treatments for patients with MM has increased substantially. Many randomized clinical trials have been conducted adding next-generation proteasome inhibitors (carfilzomib6 and ixazomib7), monoclonal antibodies (daratumumab8,9 and elotuzumab10), new immunomodulatory agents (pomaliomide11), and a histone deacetylase inhibitor (panobinostat12) to melphalan-prednisone or a 2-drug backbone of either lenalidomide-dexamethasone (LenDex) or bortezomib-dexamethasone (BorDex), which were the standard therapies for relapsed MM. In addition to these 3-drug regimens, carfilzomib-dexamethasone (CarDex)13 has been compared with BorDex in the relapsed refractory setting. The 3-drug regimens and CarDex resulted in improved progression-free survival (PFS) compared with the 2-drug regimens LenDex and BorDex. Moreover, for CarDex and carfilzomib-lenalidomide-dexamethasone (CarLenDex) an improvement in OS was observed.14,15

    Although the availability of effective regimens is embraced by both patients and hematologists, it also imposes challenges. First, as the number of treatment options increases, it is vital to know which sequence is most effective and to compare the OS and quality-adjusted life-years (QALYs) of different sequences. Unfortunately, trials investigating treatment sequences are lacking.16 Second, to preserve global access to affordable and effective therapies, it becomes increasingly important to investigate their costs and cost-effectiveness. The prices of novel agents are often high, and, in most regimens, either an expensive drug is added to standard therapy or a more expensive drug replaces standard therapy. Furthermore, many of the novel agents are not limited to a prespecified number of cycles but are administered until disease progression, further increasing the costs.17 Although cost-effectiveness studies3,18-21 for elderly patients with non–transplant-eligible (NTE) MM have been conducted, they did not investigate treatment sequences or include recently introduced agents. Therefore, we estimated the clinical effects, costs, and cost-effectiveness of treatment sequences, including all currently available novel agents, for NTE MM.

    Methods

    This study uses data from the PHAROS registry.22,23 Data collection for that registry and the use of those data for the current study were approved by the ethical committee of the Erasmus University Medical Center Rotterdam in the Netherlands, which waived the need for informed consent because the data were deidentified. This study follows the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline.

    Patient-Level Simulation Model

    A model is necessary to combine different data sources and extrapolate data to calculate lifetime costs and effects. We adapted our previously developed patient-level simulation model for elderly patients with NTE MM,3 which was based on data from a Dutch observational registry (ie, PHAROS registry).22,23 The model is a discrete event simulation consisting of objects and events.24 Objects are individual patients and are obtained from a real-world Dutch population of patients with MM aged 65 years or older and, therefore, NTE (median age at first-line treatment, 75 years); events were initiation of a new treatment line or death. Regression models per line of treatment (including coefficients for treatment, and, for the first-line treatment, patient and disease characteristics also) were used to estimate the time to event (TTE), which was defined as the time since the start of treatment to an event) (eAppendix in the Supplement). For the first- and second-line treatments, events were either the start of the next line of treatment or death. The type of event (ie, next line of treatment or death) was based on a logistic regression per line of treatment. From the start of third-line treatment, only time to death was modeled. TTE (as a proxy for time to progression) was selected as the outcome measure because the initiation of a new line of treatment or death is associated with changing costs and effects. The model simulates individual patients, each with his or her own patient and disease characteristics. For each patient, costs and effects were estimated for a maximum of 3 treatment lines according to regression models (eAppendix in the Supplement). We modeled TTE, OS (defined as time from the start of first-line treatment to death), QALYs, costs, and cost-effectiveness. Utility values (on a scale of 0 to 1, where 1 denotes perfect health, and 0 denotes death) for patients treated in clinical practice per treatment and per line of treatment are not available in the literature for patients with NTE MM. To obtain QALYs, we used, as in our previous model,3 a mean (SD) utility value of 0.76 (0.21).25

    Sequential Treatment Strategies

    Novel treatment options for newly diagnosed NTE MM and relapsed or refractory MM were identified from 2 systematic literature reviews and network meta-analyses (NMAs) of randomized phase 3 trials.26,27 Both NMAs were updated to include the most recent results from the MAIA,28 ALCYONE,29 and OPTIMISMM trials.30 To estimate the effectiveness of novel treatment sequences, the outcomes from the NMAs were combined with the Weibull regression models for lines 1, 2, and 3. Each model included a reference category for treatment (ie, line 1, melphalan-thalidomide; line 2, BorDex; and line 3, LenDex), and the relative effectiveness of the novel treatments (ie, hazard ratios [HRs] for PFS obtained from the NMAs) was used in the regression models for lines 1, 2, and 3 in the patient-level simulation model (see eFigure 1, eFigure 2, and eFigure 3 in the Supplement). The 95% CIs were used to incorporate uncertainty of treatments’ effectiveness. We identified 30 treatment sequences on the basis of the outcomes of the NMAs and clinical relevance (eTable 1 in the Supplement). We assumed that patients would not receive 2 lenalidomide-based (or 2 bortezomib-based) regimens in a treatment sequence. Hence, patients receiving lenalidomide as the first-line treatment will not receive a lenalidomide-based second-line treatment. However, because many second-line treatments include either lenalidomide or bortezomib, the possible treatment sequences are limited for first-line regimens with both lenalidomide and bortezomib (ie, bortezomib-lenalidomide-dexamethasone [VRD]) or with lenalidomide (eg, daratumumab-lenalidomide-dexamethasone [DaraLenDex]).

    Other Model Parameters
    Treatment Costs

    We retrieved dosing schemes and timing of administrations from Dutch clinical guidelines or, if not available, from randomized clinical trials (eTable 2 in the Supplement). We distinguished time periods and different costs per day for treatments with changing dosing schedules, frequencies, or regimen composition. Regimens that were given continuously (ie, until progression) were assumed to be given until the start of the next treatment or until death, whichever occurred first. For treatments with a maximum treatment duration, we used TTE or the maximum duration, whichever was shortest. Unit costs for drugs as of March 2019 were retrieved from a Dutch pharmaceutical price database (ie, including 6% value-added tax) (see eTable 3 and eTable 4 in the Supplement for details). eTable 5 in the Supplement shows detailed drug costs per day and month.

    Other Costs

    Other resource use included hospitalizations, outpatient visits, and laboratory tests. For agents requiring intravenous administration, we assumed a visit to the day ward per administration. Apart from intravenous drug administration, we retrieved average resource use in clinical practice per month by line of treatment from all elderly patients (aged ≥65 years) of the PHAROS registry (eTable 4 in the Supplement). Unit prices for hospitalization days, intravenous administrations, and outpatient visits were obtained from the Dutch costing manual31 and a Dutch MM costing study32 (eTable 5 in the Supplement).

    Statistical Analysis

    For the cost-effectiveness analysis, costs (presented in US dollars; conversion rate as of January 26, 2021, €1 = $1.2143) and effects were calculated for all treatment sequences from a hospital perspective with a lifetime horizon. The sequence bortezomib-melphalan-prednisone (VMP)–LenDex–pomalidomide-dexamethasone (PomDex) was selected as the base case for the cost-effectiveness analysis to obtain the incremental (ie, additional) effects and costs and the incremental cost-effectiveness ratio (ICER) per life-year (LY) and per QALY gained. To account for the uncertainty of the estimates, all results were obtained using probability distributions for input parameters. Input parameters for resource use and unit costs followed gamma distributions, and the utility values followed a beta distribution. The uncertainty regarding the effectiveness of the treatments was incorporated by drawing HRs from the 95% CIs and 95% credible intervals from the NMAs. We obtained mean ICERs from 5000 simulations. Costs and effects in this study were calculated by applying (1) no discount rates and (2) a discount rate of 4% for future costs and 1.5% for future effects as recommended by the Dutch Costing Manual.31

    We performed regression analysis in Stata MP statistical software version 16.1 (StataCorp), and the model was built in Excel 365 (Microsoft). Data analysis was performed from June 2019 to September 2020.

    Results
    Outcomes by Line

    Figure 1 shows the undiscounted outcomes stratified by treatment regimen for the first-, second-, and third-line treatments. These figures show the TTE in months and the annual costs, total drug costs, and total costs.

    Figure 1A and 1B show that DaraVMP is the most effective first-line regimen (mean [SD] TTE, 71 [24] months vs 53 [19] months for VMPT-VT). DaraLenDex had the highest total, annual, and drug costs. Although TTEs for VMP and LenDex were similar (32 vs 28 months), costs for LenDex were more than doubled compared with those for VMP (total costs, $281 241 vs $119 052; annual costs, $121 618 vs $45 230). The difference in costs is mainly associated with differences in treatment duration; LenDex is given until progression (ie, 28 months), whereas VMP is given with a maximum duration of 9 cycles of 35 days.

    In the second and third lines, DaraLenDex had the longest TTE and the highest mean total costs per patient but not the highest annual costs (Figure 1C and 1D and Figure 1E and 1F). Elotuzumab-lenalidomide-dexamethasone (EloLenDex) had the highest annual costs in the second line ($210 302) (Figure 1C and 1D), and pomalidomide-bortezomib-dexamethasone (PomBorDex) had the highest annual costs in the third line ($258 324) (Figure 1E and 1F).

    Figure 1C and 1D show that CarLenDex was more effective than CarDex (TTE, 33 vs 23 months). However, total costs in line 2 for CarLenDex (3-drug regimen) were lower than total costs for CarDex (2-drug regimen) ($456 492 vs $481 745). Although CarDex is given until progression (ie, 23 months), carfilzomib is discontinued after 18 cycles of 28 days each in the 3-drug regimen.

    Outcomes for Treatment Sequences

    Figure 2 shows the OS and total costs of 31 treatment sequences sorted according to survival. The first 11 sequences with daratumumab as first-line treatment (DaraVMP) or second-line treatment (ie, DaraLenDex) had similar survival outcomes (mean OS, 7.4-7.5 years, which is 3.5 additional life-years compared with VMP-LenDex-PomDex). The costs of these 11 sequences ranged from $786 024 for VMPT-VT-DaraLenDex-panobinostat-lenalidomide-dexamethasone (PanoBorDex) to $1 085 794 for DaraVMP-EloLenDex-CarDex. Compared with our base case VMP-LenDex-PomDex, the additional costs for treating 1 patient with MM may require the health care budget to increase with up to $800 000 per patient. Sequences with first-line treatment with daratumumab yielded higher mean costs compared with sequences with second-line daratumumab treatment. The sequence DaraLenDex-CarDex-PomBorDex had the highest total costs ($1 139 944). Furthermore, although DaraVMP was more effective as a first-line treatment than other first-line treatments such as VMPT-VT, treatment sequences with either DaraVMP or VMPT-VT yielded similar outcomes when DaraLenDex was given as second-line treatment.

    Because we assumed that patients would not receive 2 lenalidomide-based regimens in a sequence, the OS of the sequences VRD-CarDex-PomDex (4.6 years), DaraLenDex-CarDex-PomBorDex (5.4 years), and DaraLenDex-CarDex-PomDex (5.3 years) was lower than might have been expected on the basis of their first-line outcomes in Figure 1A and 1B. eTable 6 in the Supplement shows the number of patients with a second and third line (ie, 60%-70% of patients receiving second-line treatments and 35%-50% of patients receiving third-line treatments). eTable 6 in the Supplement also shows detailed discounted and undiscounted effects and costs per treatment sequence. LYs gained from second-line treatments were 3.3 years for sequences with CarLenDex as the second-line treatment and 2.5 years for sequences with LenDex as the second-line treatment; 0% of patients survived up to 20 years.

    ICERs for Treatment Sequence

    The Table shows the discounted incremental effects (LYs and QALYs), costs, and the ICERs compared with the base case (VMP-LenDex-PomDex), ranging from $98 585 to $3 762 906 per LY gained. The ICERs for the 11 most effective sequences ranged from $145 643 per LY gained for VMPT-VT-DaraLenDex-PanoBorDex to $232 051 per LY gained for DaraVMP-EloLenDex-PomBorDex. The most favorable sequences, with ICERs ranging from $98 585 to $99 585 per LY gained (ie, $132 707-$134 481 per QALY gained), were VMPT-VT-CarLenDex-PanoBorDex ($110 192 per QALY gained) and VMPT-VT-CarLenDex-PomDex. Apart from these sequences, all ICERs per LY gained were greater than $100 000 compared with VMP-LenDex-PomDex. For patients who cannot receive VMPT-VT as a first-line treatment, DaraVMP-CarLenDex-PanoBorDex, DaraVMP-CarLenDex-PomDex, or DaraVMP-CarLenDex-PomBorDex might be an alternative for these patients from a cost-effectiveness perspective ($195 903-$204 027 per LY gained). The sequences LenDex-CarDex–daratumumab-bortezomib-dexamethasone (DaraBorDex) and LenDex-CarDex-PomDex were dominated by the base case, which means that effectiveness of these sequences was lower, whereas the costs were higher ($568 884 vs $285 619).

    Uncertainty Analysis

    Figure 3 shows the incremental OS in months and incremental costs for each of the 5000 simulations per treatment sequence compared with the base case. The individual circles form a cloud, and the spreading of this cloud shows the uncertainty in the outcomes. Figure 3 shows that the 2 dominated sequences, LenDex-CarDex-DaraBorDex and LenDex-CarDex-PomDex, were in more than 50% of the simulations (63% and 54%, respectively) and were more expensive and less effective.

    Discussion

    To our knowledge, this study is the first to provide evidence of sequences in terms of clinical effects, costs, and cost-effectiveness in clinical practice for patients with NTE MM. We show the optimal sequences for clinical effects and cost-effectiveness, as well as relative differences. These insights provide valuable evidence additional to data from registration studies and can be used for clinical decision-making, guideline development, reimbursement decisions, and price negotiations.

    Although the cost-effectiveness of treatment sequences was not investigated previously, we can compare our results with those of other cost-effectiveness studies. The LYs for patients receiving CarLenDex and LenDex (7.83 and 5.84 years, respectively) of Jakubowiak et al20 were much higher than our estimates from second-line treatment with CarLenDex (ie, 3.3 years) and LenDex (ie, 2.5 years). This difference can be explained by the long-term OS predictions from Jakubowiak et al20; 20% of their patients treated with CarLenDex survived 20 years and 10% survived 30 years, but 0% of patients in our model survived up to 20 years. Extended follow-up of OS data indicated that the earlier predictions of Jakubowiak et al20 were too optimistic because the median OS was 4 years and 65% of patients were not alive at 6 years. Other cost-effectiveness studies are more difficult to compare because of different patient populations (eg, the differences in age of the population18 or differences in number of prior treatment lines33) and different modeling methods.18

    Modeling treatment sequences has major advantages. First, it helps to identify the optimal sequence of treatments in terms of effectiveness and to avoid suboptimal decisions. DaraVMP was the most effective first-line treatment, but sequences with this treatment in the first line have limited treatment options for the subsequent lines. For example, the most effective treatment combination in relapsed or refractory MM setting, DaraLenDex, is unlikely to be prescribed after first-line treatment with DaraVMP. As a consequence, the effectiveness of sequences starting with VMPT-VT followed by DaraLenDex yielded similar effects. Sequences with DaraVMP (followed by CarLenDex or EloLenDex as the second-line treatment) and VMPT-VT (followed by DaraLenDex as the second-line treatment) were most effective, with estimated OS up to 7.5 years. This is 3.5 additional LYs compared with VMP-LenDex-PomDex. In addition, we showed that although VRD is more effective than VMP, treatment sequences with VRD as first-line treatment had lower estimated survival. If both bortezomib and lenalidomide are already included in the first-line regime, the possible second- and third-line treatments are currently limited.

    In addition to identifying the optimal sequence in terms of effectiveness, our modeling study also provides evidence on costs. For example, total costs of the most effective treatment sequences ranged from $786 024 to $1 085 794 per patient. Compared with our base case VMP-LenDex-PomDex, the additional costs for treating 1 patient with MM may require the health care budget to increase with up to $800 000 per patient.

    Furthermore, our results show that sequences with similar effects may greatly differ in health care costs. Although the expected outcomes of the sequences VMP-LenDex-PomDex and LenDex-CarDex-PomDex were similar, the costs of the latter sequence were twice as high ($568 884 vs $285 619). This difference was mainly due to higher costs of LenDex compared with VMP as first-line treatment (total mean costs per patient were $119 052 for VMP and $281 241 for LenDex). In addition, we showed that the most effective sequences did not accrue the highest costs. The sequence DaraLenDex-CarDex-PomBorDex had the highest total costs ($1 139 944) but lower mean OS (5.4 years) compared with the most effective sequences.

    Using public list prices, we were able to provide evidence for reimbursement decisions by modeling the ICERs for treatment sequences including novel agents. These ranged from $98 585 to $3 762 906 per LY gained compared with VMP-LenDex-PomDex. Our results show that all ICERs were above currently accepted willingness-to-pay thresholds (eg, up to $96 800 [€80,000] per QALY in the Netherlands34 and £30 000 per QALY in the United Kingdom35). Sequences with first-line VMPT-VT followed by CarLenDex had the most favorable ICERs ($98 585-$110 192 for the sequences VMPT-VT-CarLenDex-PanoBorDex, VMPT-VT-CarLenDex-PomDex, and VMPT-VT-CarLenDex-PomBorDex). However, VMPT-VT is not commonly used in clinical practice, mainly because of the toxicity of long-term thalidomide administration, thereby hampering its implementation in clinical practice. From a reimbursement perspective, DaraVMP-CarLenDex-PanoBorDex, DaraVMP-CarLenDex-PomDex, or DaraVMP-CarLenDex-PomBorDex might be an alternative for these patients ($195 903-$204 027 per LY gained) because these regimens had the lowest ICERs after the sequences starting with VMPT-VT.

    The increased costs for treating MM with the novel agents necessitates either a large increase in budget or a substantial reduction of drug cost imposed by price negotiations. Our results provide crucial information for these negotiations by showing the costs of drugs and health care costs separately, and the model can recalculate the costs and cost-effectiveness for negotiated prices.

    Limitations

    This study has limitations that should be addressed. Although modeling treatment sequences provides additional information, assumptions had to be made to make a comparison of treatments that are not head-to-head comparisons. To compare the regimens, we used relative effectiveness between treatment regimens because the relative difference preserves randomization within the trials.36 According to the NMA, the HR for PFS was slightly better for VMP compared with LenDex, but with overlapping 95% CIs (VMP vs MPT, HR, 0.83 [95% CI, 0.46-1.51; LenDex vs MPT, HR, 0.94 [95% CI, 0.67-1.30]).36 Because the relative effectiveness of DaraVMP compared with VMP was more favorable (HR for PFS in the ALCYONE trial, 0.42)29 compared with the relative effectiveness of DaraLenDex compared with LenDex (HR for PFS in the MAIA trial, 0.56),29 DaraVMP shows better outcomes in our model compared with DaraLenDex. It should be noted that absolute PFS obtained from comparisons in phase 3 trials are more favorable for continuous LenDex-based regimens (eg, LenDex, 21-39 months; VRD, 43 months; and DaraLenDex, not reached) compared with bortezomib-based regimens (VMP, 17.3-32 months; VMPT-VT, 35 months; and DaraVMP, 36.4 months).36 Future research, ideally a head-to-head comparison of DaraVMP and DaraLenDex, should confirm whether DaraVMP is indeed more effective.

    We assumed that the HR for PFS would be representative for the HR for TTE to use the NMA outcomes. Although we cannot verify whether this assumption is valid for all treatments, it is supported by the results from the VISTA trial (HR for VMP vs MP for PFS, 0.56 [P < .001]; HR for time to next treatment, 0.52 [P < .001])37 and GIMEMA0305 trial (VMPT-VT vs VMP, HR, 0.58 [P < .001] and time to next treatment, HR, 0.52 [P < .001])38. The number of patients decreases per line of treatment, which is based on the association between TTE and the type of event, as observed in Dutch clinical practice data (ie, PHAROS data). This is, however, comparable to proportions reported for other European countries.5 We assumed that the association between TTE and type of event also exists for the novel therapies. Future clinical practice data should confirm this modeled proportion for novel treatments (60%-70% for second-line treatments and 35%-50% for third-line treatments).

    In addition, the actual acquisition price may be lower in other countries because of negotiated discounts. Our results (Figure 1) show that drug costs are the major cost factor and illustrate the sensitivity of our results to the drug prices. If the negotiated price reduction has a similar ratio for the drugs, the order of the results will not substantially differ. An advantage of our model is that negotiated discounted prices and prices from other countries can be easily incorporated.

    Conclusions

    The findings of this economic evaluation show the relevance of investigating treatment sequences and may help to improve the quality of care for patients with NTE MM and the efficiency of health care delivery. In the context of increasing health care expenditures, evidence on the cost-effectiveness of treatments and on treatment sequences is crucial for ensuring efficient use of limited resources.

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    Article Information

    Accepted for Publication: February 8, 2021.

    Published: March 29, 2021. doi:10.1001/jamanetworkopen.2021.3497

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Blommestein HM et al. JAMA Network Open.

    Corresponding Author: Hedwig M. Blommestein, PhD, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3000 DR Rotterdam, the Netherlands (blommestein@eshpm.eur.nl).

    Author Contributions: Dr Blommestein had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Franken and Ms van Beurden-Tan contributed equally to this work.

    Concept and design: Blommestein, Franken, Blijlevens, Huijgens, Uyl-de Groot, Zweegman.

    Acquisition, analysis, or interpretation of data: Blommestein, Franken, van Beurden-Tan, Blijlevens, Sonneveld, Uyl-de Groot, Zweegman.

    Drafting of the manuscript: Blommestein, Franken, Blijlevens, Uyl-de Groot, Zweegman.

    Critical revision of the manuscript for important intellectual content: Franken, van Beurden-Tan, Blijlevens, Huijgens, Sonneveld, Uyl-de Groot, Zweegman.

    Statistical analysis: Blommestein, Huijgens, Uyl-de Groot.

    Obtained funding: Uyl-de Groot.

    Administrative, technical, or material support: Blommestein, van Beurden-Tan, Sonneveld, Uyl-de Groot.

    Supervision: Blijlevens, Huijgens, Sonneveld, Uyl-de Groot.

    Conflict of Interest Disclosures: Dr Blommestein reported receiving grants from Celgene BV outside the submitted work. Dr Franken reported receiving grants from Roche Netherlands BV, Daiichi Sankyo, AbbVie, PamGene, Gilead Sciences Netherlands BV, Astellas Pharma BV, and Svenska Cellulosa Aktiebolaget outside the submitted work. Dr van Beurden-Tan reported receiving personal fees from Hematology Newsboard outside the submitted work. Dr Uyl-de Groot reported receiving grants from Amgen and Janssen-Cilag and personal fees from Celgene and Novartis outside the submitted work. Dr Zweegman reported receiving research grants and reimbursement for serving on advisory boards from Janssen, Takeda, and Celgene outside the submitted work. No other disclosures were reported.

    Funding/Support: This work was supported by a grant from ZonMw, the Netherlands Organisation for Health Research and Development, project number 152001020, “Treatment Sequencing in Multiple Myeloma: Modeling the Disease and Evaluating Cost-efficacy vs. Costeffectiveness.” This study used data from the PHAROS Dutch population-based registry, which was financially supported by ZonMW 2009-2012 (grant 80-82500-98-01007) and through unrestricted grants from Bayer, Roche, Janssen, Celgene, Sanofi, MSD, Novartis, GSK, Mundipharma, and BMS.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Information: All data generated or analyzed during this study for the development of the model are included in this published article and its supplemental material. The underlying data for the analysis in this study are available from the Netherlands Cancer Registry, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the Netherlands Cancer Registry.

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