Cost-Effectiveness Analysis of Daratumumab Monotherapy and Subsequent Therapies in Heavily Treated Relapsed/Refractory Multiple Myeloma: A Feasible Methodology using a Korean Nationwide Population Cohort

Sung-Soo Park; Suein Choi; Seungpil Jung; Seunghoon Han; Chaehyeon Lee; Jinseon Han; Soyoung Kim; Kihyun Kim; Chang-Ki Min

doi:10.4143/crt.2025.046

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Original Article Hematologic malignancy Cost-Effectiveness Analysis of Daratumumab Monotherapy and Subsequent Therapies in Heavily Treated Relapsed/Refractory Multiple Myeloma: A Feasible Methodology using a Korean Nationwide Population Cohort: Sung-Soo Park^1,2, Suein Choi^2,3,4, Seungpil Jung^2,3,4, Seunghoon Han^3,4, Chaehyeon Lee^2,3,4, Jinseon Han^3,4, Soyoung Kim⁵, Kihyun Kim⁶, Chang-Ki Min¹; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2026;58(1):300-310.
DOI: https://doi.org/10.4143/crt.2025.046
Published online: April 15, 2025

¹Department of Hematology, Catholic Hematology Hospital, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

²Division of Data Science, Catholic Research Network for Multiple Myeloma, Seoul, Korea

³Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seoul, Korea

⁴Pharmacometrics Institute for Practical Education and Training (PIPET), College of Medicine, The Catholic University of Korea, Seoul, Korea

⁵Innovation Center for Industrial Mathematics, National Institute for Mathematical Sciences, Seongnam, Korea

⁶Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

Correspondence: Seunghoon Han, Department of Pharmacology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea
Tel: 82-2-2258-7326 E-mail: waystolove@catholic.ac.kr

Co-correspondence: Kihyun Kim, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea
Tel: 82-2-3410-3456 E-mail: kihyunkimk@gmail.com

^*Sung-Soo Park and Suein Choi contributed equally to this work.

• Received: January 10, 2025 • Accepted: April 13, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Purpose
High-cost novel therapies for multiple myeloma (MM) require evaluation of efficacy and cost-effectiveness.
Materials and Methods
This study developed a methodology to assess cost-effectiveness using nationwide data from 11,450 newly diagnosed MM patients. A novel algorithm was applied to identify lines of therapy (LoT).
Results
The number of newly diagnosed MM patients increased significantly, from 873 in 2010 to 1,464 in 2019 (p < 0.001). Advancing LoT was associated with shorter time to next treatment (TTNT) and overall survival (OS) (p < 0.001), while all-cause medical costs increased with each LoT (p < 0.001). Bortezomib-melphalan-prednisolone was the most common frontline regimen for transplant-ineligible patients (29.2%), while bortezomib-thalidomide-dexamethasone was most used for transplant-eligible patients (11.3%). Daratumumab monotherapy demonstrated superior second TTNT (7.8 vs. 5.2 months) and OS (8.5 vs. 5.3 months) compared to standard care in heavily treated MM patients, with statistical significance maintained after cost adjustment. For subsequent therapies following daratumumab, a methodology was developed to estimate required medical costs using the incremental cost-effectiveness ratio (ICER): Expected cost ($)=ICER×(Expected life expectancy–0.567)+35,601.
Conclusion
This study provides a novel cost-effectiveness framework linking treatment efficacy and real-world costs, supporting predictions of societal costs for future MM therapies.
Key words: Daratumumab, Cost-effectiveness, Multiple myeloma, Adoptive immunotherapy, Bispecific antibodies

Introduction

Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells in the bone marrow [1]. Over the past two decades, the treatment landscape for MM has significantly improved with the emergence of new therapies and combinations of existing treatment modalities integrated into evolving treatment guidelines [2,3]. The introduction of novel agents, such as proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and monoclonal antibodies has led to remarkable progress in treatment outcomes [4,5].

Despite advances in treatment, MM remains incurable, necessitating the development of new therapies for disease control [6]. Over the disease course, patients inevitably experience relapse and require multiple salvage treatments. As new agents and an increasing array of combination therapies are introduced, implementing methodologies to compare the real-world effectiveness of these treatments and inform optimal decision-making becomes critically important. Evidence-based tools are essential for rigorously evaluating whether new therapies provide superior efficacy compared to existing options. Additionally, the introduction of novel agents often significantly increases healthcare costs [7]. Given these challenges, the development and application of objective methodologies that offer quantifiable metrics of treatment efficiency, considering both clinical outcomes and the economic impact of new therapies, are urgently needed.

To address this gap, we used the nationwide Health Insurance Review and Assessment (HIRA) database, which covers approximately 98% of the Korean population and provides comprehensive records of healthcare utilization and medical costs [8]. This extensive coverage enables the formation of a large cohort of patients with rare diseases, such as MM, facilitating the identification of sequential treatment patterns. By leveraging this cohort, we aimed to develop methodologies for monitoring and validating newly introduced treatment strategies in real-world clinical settings. Specifically, we focused on comparing daratumumab monotherapy, which was recently approved for public insurance coverage in South Korea, with the standard of care (SOC) for MM and successfully applied our methodology. Furthermore, with the anticipated introduction of ultra-high-cost therapies, such as chimeric antigen receptor T cells (CAR-T) cells and bispecific antibodies (BiAbs) [9], we aimed to establish a cost-effectiveness reference for SOC treatments administered after daratumumab monotherapy. This will provide a valuable foundation for future decision-making regarding the adoption of these emerging therapies.

Materials and Methods

1. Study population, design, and data source

This retrospective observational study used the HIRA database containing records from January 1, 2007, through December 31, 2020. We focused on patients newly diagnosed with MM between January 1, 2010, and December 31, 2019, to allow for a 3-year washout period (2007-2009) that excluded pre-existing cases, as well as at least one year of follow-up for all included patients. We initially identified 15,237 adult patients (aged 19 years or older) who had been treated for MM, and from these, final cohort of 11,450 newly diagnosed patients with MM (Fig. 1A) was selected based on the prespecified inclusion and exclusion criteria (see Supplementary Methods). All analyses were conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and relevant sections of the Consolidated Standards of Reporting Trials guidelines.

2. Development of methodology for identification of MM drugs and line of therapy

Drugs used for MM treatment were identified using HIRA-reimbursed drug codes linked to the World Health Organization-Anatomical Therapeutic Chemical classification, as outlined in S1 Table. To define the line of therapy (LoT) for MM treatments, we adapted an algorithm based on existing literature [10,11]. This algorithm was further refined to account for the specific insurance-eligible therapeutic environment for MM in South Korea (Fig. 1B). Subsequent LoTs were considered under the following conditions: (1) when a patient was treated with an MM drug not included in the regimen used within the first 21 days of initiating the previous LoT, or (2) when the same treatment was resumed after a drug-free period exceeding 180 days from the last prescription date of the drug in the previous LoT (i.e., retreatment with the same MM drugs after more than 180 days). Cytotoxic agents were excluded from the defining LoTs when used for stem cell mobilization or conditioning for hematopoietic stem cell transplantation (HSCT) [12-14]. Specifically, drug codes for etoposide (e), cyclophosphamide (C), or melphalan (M) appearing within 30 days prior to the index date of mobilization or before HSCT were excluded from the LoT definition. Definitions for the index dates of stem cell mobilization and HSCT are provided in S2 Table. The regimen for each LoT was defined based on the combination of key drug codes (S3 Table).

3. Subgroup construction of daratumumab monotherapy as the case cohort and SOC group as the control cohort

From the database, we identified 1,431 patients with sequential data for the fourth LoT. Among these, the case cohort included patients who received daratumumab monotherapy, while the control cohort comprised those treated with the SOC as their fourth, fifth, or sixth LoT, respectively. To ensure compliance with the approved indications for daratumumab monotherapy in South Korea [15,16], patients in these subgroups who had not been exposed to both bortezomib and lenalidomide were excluded. For the case cohort, the initiation of daratumumab monotherapy was defined as the index date. For the control cohort, the index date was defined as the start date of the most recent SOC treatment at the corresponding LoT, as shown in S4 Fig. Detailed eligibility criteria for the subgroup studies, including patients with subsequent LoT following daratumumab, are in the Supplementary Methods.

4. Definitions and statistical analysis

We analyzed treatment patterns and outcomes, focusing on real-world treatment sequences in Korea, time to next line of therapy (TTNT), time to second subsequent line of therapy (TTNT2), and overall survival (OS), with detailed definitions provided in the Supplementary Methods.

Healthcare resource utilization (HCRU) and costs during TTNT were assessed for each LoT and reported as per person per month (PPPM). All-cause HCRU included hospitalizations as an inpatient and medical visits as an outpatient. All-cause healthcare costs included expenses associated with claims recorded during the follow-up period.

With claims data available for all patients from 2007 onwards, we ensured that each patient had 3 years of comorbidity data tracked from their date of MM diagnosis. Comorbidity profiling was performed using the Charlson Comorbidity Index (CCI), with ICD-10 (International Classification of Diseases, 10th revision) codes applied as described in S5 Table [17]. In-depth details of the statistical analyses, including the hypothesis testing procedures, weighting methods, and cost-effectiveness modeling, are provided in the Supplementary Methods.

Results

1. Demographic and clinical characteristics in the total cohort

The demographic and clinical characteristics of the total cohort are summarized in S6 Table. The number of patients newly diagnosed with MM showed a significant increasing trend over time, rising from 873 (7.6%) in 2010 to 1,464 (12.8%) in 2019. Among the total study population, 42.4% were aged 70 years or older, and 54.4% were male. With a median follow-up period of 59.6 months (95% confidence interval [CI], 58.3 to 60.8), the median OS for the total cohort was 43.0 months (95% CI, 41.6 to 44.5) (S7 Fig.). Regarding the number of patients per LoT, 9,825 (85.8%) had data on first LoT (LoT1), 5,346 (46.7%) on second LoT (LoT2), 2,759 (24.1%) on third LoT (LoT3), 1,431 (12.5%) on fourth LoT (LoT4), 709 (6.2%) on fifth LoT (LoT5), and 330 (2.8%) on sixth LoT (LoT6). Both TTNT and OS significantly declined as the LoT advanced (all p < 0.001) (Fig. 2A and B). The median TTNT and OS were as follows: LoT1, 16.5 months (95% CI, 15.8 to 17.1) and 42.2 months (95% CI, 40.8 to 43.8); LoT2, 11.8 months (95% CI, 11.3 to 12.3) and 27.8 months (95% CI, 26.7 to 29.2); LoT3, 7.8 months (95% CI, 7.3 to 8.2) and 17.9 months (95% CI, 16.4 to 19.4); LoT4, 6.2 months (95% CI, 5.6 to 6.7) and 13.4 months (95% CI, 12.1 to 14.6); LoT5, 5.3 months (95% CI, 4.6 to 6.1) and 11.3 months (95% CI, 9.8 to 12.9) months; and LoT6, 5.1 months (95% CI, 4.6 to 5.7) and 8.6 months (95% CI, 7.0 to 10.5), respectively.

HCRU and cost across the follow-up period were 3.6 (±2.8) visits and $4,486.8 (±$5,122.6) PPPM, respectively. During TTNT of each LoT, all-cause medical costs and HCRU, including inpatient and outpatient visits, increased progressively with each higher LoT compared to LoT1 (p < 0.001) (Fig. 2C, S8 Table). As shown in Fig. 2D, all-cause medical costs increased significantly with each advancing LoT (p < 0.001).

S9 Table presents the top 20 most common treatment sequences observed in the total cohort, ranked by frequency. Most top-ranked sequences were terminated by LoT2. Among these sequences, half (n=10/20) concluded with death. The most frequently used LoT1 regimen was VMp (bortezomib-melphalan-prednisolone) (n=2,873/9,825, 29.2%), followed by VTd (bortezomib-thalidomide-dexamethasone) with or without HSCT (n=1,106/9,825, 11.3%).

2. Subgroup study 1: Comparison of outcomes between the daratumumab monotherapy cohort (case cohort) and SOC cohort (control cohort)

After confirming the methodology for defining LoT, conducting survival analyses, and collecting cost data for the entire cohort, we conducted survival and cost-effectiveness analyses in the targeted subgroup. From the study population, we selected 273 patients for the daratumumab monotherapy cohort (case cohort) and 701 patients for the SOC cohort (control cohort). Table 1 presents the demographic and clinical characteristics of the two subgroups. The proportion of male patients was 54.2%, and the largest age group was 59 years or younger (38.4%), with only 2.3% of patients aged 80 or older. Most patients were diagnosed with MM in 2013 (17.2%) or 2014 (14.5%), with 48.9% entering the cohort at LoT4. The highest proportion of patients had a CCI score of 0 or 1 (31.0%). In the CCI categories, the most common comorbidity was chronic pulmonary disease, affecting 53.4% of patients (n=520), followed by peptic ulcer disease (32.2%) and mild liver disease (23.5%). The mean interval from diagnosis to the index date was 4.13 (±1.99) years. Notably, confounding variables were successfully balanced, with standardized mean differences ≤ 0.1 after inverse probability of treatment weighting adjustment, as shown in S10 Table and S11 Fig.

The median follow-up duration was 10.0 months (95% CI, 9.0 to 12.7) for the weight-adjusted case cohort and 22.0 months (95% CI, 20.9 to not reached) for the weight-adjusted control cohort. TTNT was not significantly different between the weight-adjusted case and control cohorts (Fig. 3A): the median TTNT was 4.1 months (95% CI, 3.6 to 4.8) in the weight-adjusted case cohort versus 5.0 months (95% CI, 4.4 to 5.4) in the weight-adjusted control cohort, with a hazard ratio (HR) of 1.10 (95% CI, 0.92 to 1.31). Both TTNT2 and OS were significantly better in the weight-adjusted case cohort compared to the weight-adjusted control cohort: the median TTNT2 was 7.8 months (95% CI, 6.3 to 9.8) in the weight-adjusted case cohort vs. 5.2 months (95% CI, 4.7 to 5.8) in the weight-adjusted control cohort, with an HR of 0.73 (95% CI, 0.60 to 0.89) (Fig. 3B). Similarly, the median OS was 8.5 months (95% CI, 6.4 to 11.6) in the weight-adjusted case cohort versus 5.3 months (95% CI, 4.8 to 5.9) in the weight-adjusted control cohort, with an HR of 0.68 (95% CI, 0.56 to 0.84) (Fig. 3C).

The cost-effectiveness analysis, presented in Table 2, compares daratumumab monotherapy with SOC in terms of life years gained and associated medical costs. Daratumumab monotherapy resulted in a mean life expectancy of 0.986 life years, providing an incremental gain of 0.192 life years over the 0.794 life years with SOC. However, this increased weighted survival comes at a higher cost, with the mean total cost for daratumumab being $57,176 compared to $40,987 for SOC, resulting in an incremental cost of $16,189. The ICER was calculated to be $84,385 ($16,189/0.192) per life year gained, indicating that while daratumumab monotherapy provides a survival advantage, it does so at a significant additional cost. Nonetheless, the weighted Cox proportional hazard analysis demonstrated that daratumumab monotherapy still showed superior weighted survival outcomes for TTNT2 (HR, 0.58; 95% CI, 0.47 to 0.70) and OS (HR, 0.54; 95% CI, 0.44 to 0.66), even after adjusting for medical costs (Fig. 4).

3. Subgroup study 2: A cohort consisting of patients treated with subsequent LoT following daratumumab monotherapy

From the daratumumab monotherapy cohort, we identified 88 patients who were treated with a subsequent LoT. Baseline characteristics of this subgroup are presented in S12 Table. With a median follow-up duration of 8.1 months (95% CI, 6.4 to 12.6), the median TTNT and OS were 4.6 months (95% CI, 3.6 to 7.0) and 6.8 months (95% CI, 4.7 to 10.7) from the date of initiation of subsequent LoT after daratumumab monotherapy, respectively, as shown in Fig. 5. The mean life year and mean total cost incurred were calculated as 0.567 and $35,601, respectively. Based on this data, in the context of introducing new drugs with indications that include prior exposure to daratumumab monotherapy, the cost of a new drug (C), calculated based on the cost per life year gained (L), can be suggested as shown in Fig. 6.

Discussion

This large, population-based observational study provides valuable insights into real-world treatment patterns, outcomes, and the economic burden of MM in South Korea using patient-level data and a validated algorithm for identifying LoT. Our study systematically demonstrates trends across LoTs and validates these findings through comparisons with existing literature. First, we observed an increasing annual incidence of MM in South Korea, from 873 cases in 2010 to 1,464 in 2019, consistent with national cancer statistics [18]. Second, survival outcomes declined as LoTs advanced, with median TTNT and OS decreasing, in alignment with prior multi-country studies showing a steady decrease in time to progression across LoTs [19]. Third, treatment patterns reflected South Korean reimbursement policies during the study period. Novel frontline therapies widely adopted globally, such as bortezomib-lenalidomide-dexamethasone and daratumumab-VTd, were unavailable due to reimbursement restrictions, while VMp and VTd regimens were predominantly utilized [20,21]. We also evaluated the economic burden associated with advanced LoTs, confirming that HCRU and costs escalated significantly with disease progression. Similar trends were reported in retrospective analyses using U.S. claims databases, which demonstrated that costs were substantially higher in patients progressing to later LoTs [22,23]. Given the increased burden of treating relapsed or refractory MM, incorporating effective therapies earlier in the treatment course might optimize clinical outcomes while reducing long-term costs [24,25].

In a subgroup analysis, we demonstrated the superior cost-effectiveness and clinical benefits of daratumumab monotherapy compared to SOC. Daratumumab is an anti-CD38 human IgG1 monoclonal antibody approved for monotherapy in patients with MM who have received three or more prior treatments, including a PI and an IMiD [15]. Although daratumumab monotherapy’s efficacy has been validated in phase 1 and phase 2 trials and through real-world evidence studies [15,26,27], no phase 3 comparative trials have been conducted to date. Therefore, real-world evidence remains critical for evaluating this treatment option. Our study stands out as it builds a nationwide cohort of patients treated with daratumumab monotherapy and demonstrates, through systematic real-world data analysis, that daratumumab monotherapy offers significantly better TTNT2 and OS outcomes compared to SOC. These benefits were maintained even after adjusting for medical costs. While daratumumab monotherapy’s efficacy in terms of TTNT did not reach statistical significance, this treatment demonstrated superior outcomes in TTNT2 and OS, likely reflecting the pharmacological properties of daratumumab highlighted in previous literature. Previous studies, including our own prior reports and research by Oostvogels et al. [15,28], have shown that daratumumab monotherapy provided benefits even when patients are retreated with PI or IMiD to which they have previously been exposed. This may be attributed to daratumumab’s ability to maintain anti-CD38 effects for up to 6 months following the last infusion [29]. To our knowledge, this is the first study to provide reliable comparative data on these additional effects of daratumumab versus SOC. Moreover, our findings indicate that after adjusting for medical costs, the positive effects of daratumumab monotherapy become even more pronounced: the HR for TTNT2 improved from 0.727 to 0.577, and for OS from 0.684 to 0.536 following medical cost adjustment. Despite the absence of phase 3 trials, this real-world evidence suggests that daratumumab monotherapy provides meaningful clinical benefits for patients who have previously been exposed to both bortezomib and lenalidomide and supports its continued use in routine clinical practice.

The emergence of immunotherapeutic options, including CAR-T cell therapies and BiAbs, offers promising alternatives for patients refractory to daratumumab [30-32]. In South Korea, where daratumumab is reimbursed only as monotherapy, CAR-T and BiAb therapies are becoming relevant options for patients who did not respond to daratumumab monotherapy. Therefore, it is critical to evaluate the effectiveness and medical costs of the subsequent therapies following daratumumab monotherapy in this context. Our study meets this need by providing data on the outcomes and costs of subsequent SOCs after daratumumab monotherapy. These findings offer valuable insights to inform decision-making regarding the introduction of CAR-T therapies and BiAbs into the South Korean healthcare system. Additionally, these data establish a foundation for cost-effectiveness analysis, which can guide estimates of societal costs associated with these therapies and support policy decisions regarding their reimbursement. This study demonstrates the feasibility of identifying indications for new therapies within the HIRA database and standardizing the variables for cost-effectiveness analysis, including ICER and expected mean life years. The methodologies applied in this study can also extend to other cancers, facilitating standardized evaluations of novel therapies’ cost-effectiveness and clinical impact. This real-world evidence provides policymakers and clinicians with a robust framework for assessing societal costs and treatment feasibility.

Despite the strengths of the population-based real-world data used in this study, the inherent limitations of such databases should be carefully considered when interpreting the results. A comprehensive LoT identification algorithm was developed to reflect real-world, local clinical practices accurately. However, not all scenarios may have been captured by the algorithm, potentially leading to the misclassification of drugs within regimens and LoT sequences. In particular, due to the nature of claims data, MM drugs not covered by healthcare insurance were excluded from this study. To minimize potential misclassification and discrepancies, the algorithm was based on a validated framework and further refined through extensive consultations with local clinicians. Nevertheless, some degree of misclassification may still exist, which could impact the generalizability of the findings.

In summary, this study provides real-world insights into the evolving treatment landscape, outcomes, and economic burden of MM in South Korea. Our findings confirm the cost-effectiveness and clinical benefit of daratumumab monotherapy compared to SOC and highlight the need for effective treatment strategies to be incorporated earlier in the disease course. Furthermore, this study establishes a framework for evaluating new therapies such as CAR-T cells and BiAbs, offering a foundation for evidence-based policymaking and cost-effective treatment decisions in MM.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2025-046_Supplementary_Methods.pdf

crt-2025-046_S1_Table.pdf

crt-2025-046_S2_Table.pdf

crt-2025-046_S3_Table.pdf

crt-2025-046_S4_Fig.pdf

crt-2025-046_S5_Table.pdf

crt-2025-046_S6_Table.pdf

crt-2025-046_S7_Fig.pdf

crt-2025-046_S8_Table.pdf

crt-2025-046_S9_Table.pdf

crt-2025-046_S10_Table.pdf

crt-2025-046_S11_Fig.pdf

crt-2025-046_S12_Table.pdf

NOTES

Ethical Statement

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. This study received approval from the Institutional Review Board of Seoul St. Mary’s Hospital, Seoul, Korea (No. KC21ZNSI0448) and Samsung Medical Center (SCM 2022-01-151). As the data were anonymized and de-identified, the requirement for informed consent was waived.

Author Contributions

Conceived and designed the analysis: Han S, Kim K, Min CK.

Collected the data: Park SS, Choi S, Jung S, Lee C, Han J.

Contributed data or analysis tools: Park SS, Choi S, Jung S, Lee C, Han J.

Performed the analysis: Park SS, Choi S, Jung S, Lee C, Han J.

Wrote the paper: Park SS, Choi S, Jung S, Kim S.

Interpretation of data: Park SS, Choi S, Jung S.

Conflicts of Interest

The authors have received research grants from Janssen Korea Ltd.

Funding

This research was supported by a grant (RS-2023-00216446) from Ministry of Food and Drug Safety in 2023. S. Kim was supported by the National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government (MSIT) (No. NIMS-B24810000).

Acknowledgments

This study was funded by Janssen Korea Ltd. We would like to express our gratitude to Yong Ju Park and Soomin Yoon for their dedication and contributions to the progression of this research.

Fig. 1.

Flowchart illustrating the study population selection process (A) and line of therapy (LoT) identification process (B). ^a)New claims, any new medication claims filed for the patient, ^b)New MM therapy: the introduction of the specified therapy for MM that was not part of the patient’s previous regimen.

Fig. 2.

Survival outcomes and medical costs following each line of therapy (LoT). (A) Time to next treatment for each LoT. (B) Overall survival for each LoT. (C) All-cause medical costs between each LoT and the subsequent LoT. (D) Trend of increasing medical costs over time with advancing LoTs. MM, multiple myeloma; PPPM, per per son per month.

Fig. 3.

Weighted survival curves between the daratumumab monotherapy cohort (weight-adjusted case cohort) and standard of care cohort (weight-adjusted control cohort). (A) Time to next treatment. (B) Time to second subsequent line of therapy. (C) Overall survival. CI, confidence interval; HR, hazard ratio; SOC, standard of care.

Fig. 4.

Medical cost-adjusted weighted survival curves between the daratumumab monotherapy cohort (medical cost and weight-adjusted case cohort) and standard of care cohort (medical cost and weight-adjusted control cohort) based on the weight Cox proportional hazard analysis. (A) Time to next treatment. (B) Time to second subsequent line of therapy. (C) Overall survival. CI, confidence interval; HR, hazard ratio; SOC, standard of care.

Fig. 5.

Survival outcome analysis in a subgroup of patients who received the next line of therapy following daratumumab monotherapy. (A) Time to next treatment. (B) Overall survival.

Fig. 6.

Expected cost of a new drug (C) indicating failure after daratumumab monotherapy based on the cost per life year gained (L). In the cost-effectiveness analysis, the cost per life year gained (incremental cost-effectiveness ratio, ICER) is calculated as incremental mean life years divided by mean total cost. For the subsequent line of therapy after daratumumab, the mean life years and mean total cost were 0.567 and $35,601, respectively. The incremental mean life years for a new drug would be (L–0.567), and the incremental mean total cost would be (C–35,601). Therefore, ICER=(C–35,601)/(L–0.567). To estimate the mean total cost of the new drug, the equation can be rearranged as C (in $)=ICER×L–12,245 (in $). Considering the ICER of $84,385 observed in the cost-effectiveness analysis of daratumumab monotherapy, we assume that the new drug should achieve at least an ICER of $84,385. Therefore, C (in $) ≥ 84,385×L–12,245 (in $). By determining the value of L based on the characteristics of the new drug, this formula provides a basis for estimating the minimum medical cost. As shown in the f igure, for expected mean life years of 0.7, 0.8, and 0.9, the minimum medical costs are $46,824, $55,263, and $63,701, respectively (dashed line). Note: Mean life years (L) is calculated using a semi-Markov multi-state modeling approach with the Weibull distribution.

Table 1.

Baseline characteristics of patients treated with case cohort (daratumumab monotherapy) and control cohort (SOC)

Characteristic	Overall (n=974)	Case cohort (n=273)	Control cohort (n=701)	p-value
Sex
Male	528 (54.2)	132 (48.4)	396 (56.5)	0.03
Female	446 (45.8)	141 (51.6)	305 (43.5)
Age (yr)
19-59	374 (38.4)	99 (36.3)	275 (39.2)	0.37
60-69	370 (38.0)	99 (36.3)	271 (38.7)
70-79	208 (21.4)	68 (24.9)	140 (20.0)
≥ 80	22 (2.3)	7 (2.6)	15 (2.1)
Year at diagnosis of multiple myeloma
2010	73 (7.5)	4 (1.5)	69 (9.8)	< 0.001
2011	70 (7.2)	8 (2.9)	62 (8.8)
2012	125 (12.8)	19 (7.0)	106 (15.1)
2013	168 (17.2)	24 (8.8)	144 (20.5)
2014	141 (14.5)	32 (11.7)	109 (15.5)
2015	116 (11.9)	35 (12.8)	81 (11.6)
2016	123 (12.6)	51 (18.7)	72 (10.3)
2017	83 (8.5)	47 (17.2)	36 (5.1)
2018	49 (5.0)	37 (13.6)	12 (1.7)
2019	26 (2.7)	16 (5.9)	10 (1.4)
Index of line of therapy
4	476 (48.9)	133 (48.7)	343 (48.9)	0.20
5	296 (30.4)	92 (33.7)	204 (29.1)
6	202 (20.7)	48 (17.6)	154 (22.0)
Diagnosis to index date (yr)	4.13±1.99	4.11±2.07	4.14±1.96	0.85
Total medical cost ($)	12,498.0±11,122.2	15,333.1±11,973.4	11,393.8±10,577.6	< 0.001
Charlson Comorbidity Index score
0 to 1	302 (31.0)	88 (32.2)	214 (30.5)	0.71
2	237 (24.3)	69 (25.3)	168 (24.0)
3	160 (16.4)	39 (14.3)	121 (17.3)
4 or higher	275 (28.2)	77 (28.2)	198 (28.2)
Comorbidities at index date
Myocardial infarction	11 (1.1)	2 (0.7)	9 (1.3)	0.69
Congestive heart failure	109 (11.2)	32 (11.7)	77 (11.0)	0.83
Peripheral vascular disease	96 (9.9)	32 (11.7)	64 (9.1)	0.27
Cerebrovascular disease	96 (9.9)	25 (9.2)	71 (10.1)	0.73
Dementia	18 (1.8)	6 (2.2)	12 (1.7)	0.81
Chronic pulmonary disease	520 (53.4)	144 (52.7)	376 (53.6)	0.86
Connective tissue disease	34 (3.5)	11 (4.0)	23 (3.3)	0.71
Peptic ulcer disease	314 (32.2)	81 (29.7)	233 (33.2)	0.32
Mild liver disease	229 (23.5)	67 (24.5)	162 (23.1)	0.70
Diabetes without chronic complication	311 (31.9)	87 (31.9)	224 (32.0)	0.99
Diabetes with chronic complication	101 (10.4)	23 (8.4)	78 (11.1)	0.26
Hemiplegia or paraplegia	18 (1.8)	3 (1.1)	15 (2.1)	0.41
Renal disease	132 (13.6)	43 (15.8)	89 (12.7)	0.25
Any malignancy, including lymphoma and leukemia, except malignant neoplasm of skin	142 (14.6)	38 (13.9)	104 (14.8)	0.79
Moderate or severe liver disease	3 (0.3)	0	3 (0.4)	0.66
Metastatic solid tumor	11 (1.1)	1 (0.4)	10 (1.4)	0.29
AIDS/HIV	1 (0.1)	1 (0.4)	0	0.63

Values are presented as number (%) or mean±SD. AIDS/HIV, acquired immune deficiency syndrome/human immunodeficiency virus; SD, standard deviation; SOC, standard of care.

Table 2.

Cost-effectiveness analysis

	Daratumumab monotherapy	Standard of care	Incremental
Mean life years	0.986	0.794	0.192
Mean total cost ($)	57,176	40,987	16,189
Cost per life year gained (ICER, $)			84,385^a)

^a) The incremental cost-effectiveness ratio (ICER) is $84,385, which is calculated by dividing the incremental cost ($16,189) by the incremental life years (0.192).

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Cost-Effectiveness Analysis of Daratumumab Monotherapy and Subsequent Therapies in Heavily Treated Relapsed/Refractory Multiple Myeloma: A Feasible Methodology using a Korean Nationwide Population Cohort

Cancer Res Treat. 2026;58(1):300-310. Published online April 15, 2025

DOI: https://doi.org/10.4143/crt.2025.046

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Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Fig. 1. Flowchart illustrating the study population selection process (A) and line of therapy (LoT) identification process (B). a)New claims, any new medication claims filed for the patient, b)New MM therapy: the introduction of the specified therapy for MM that was not part of the patient’s previous regimen.

Fig. 2. Survival outcomes and medical costs following each line of therapy (LoT). (A) Time to next treatment for each LoT. (B) Overall survival for each LoT. (C) All-cause medical costs between each LoT and the subsequent LoT. (D) Trend of increasing medical costs over time with advancing LoTs. MM, multiple myeloma; PPPM, per per son per month.

Fig. 3. Weighted survival curves between the daratumumab monotherapy cohort (weight-adjusted case cohort) and standard of care cohort (weight-adjusted control cohort). (A) Time to next treatment. (B) Time to second subsequent line of therapy. (C) Overall survival. CI, confidence interval; HR, hazard ratio; SOC, standard of care.

Fig. 4. Medical cost-adjusted weighted survival curves between the daratumumab monotherapy cohort (medical cost and weight-adjusted case cohort) and standard of care cohort (medical cost and weight-adjusted control cohort) based on the weight Cox proportional hazard analysis. (A) Time to next treatment. (B) Time to second subsequent line of therapy. (C) Overall survival. CI, confidence interval; HR, hazard ratio; SOC, standard of care.

Fig. 5. Survival outcome analysis in a subgroup of patients who received the next line of therapy following daratumumab monotherapy. (A) Time to next treatment. (B) Overall survival.

Fig. 6. Expected cost of a new drug (C) indicating failure after daratumumab monotherapy based on the cost per life year gained (L). In the cost-effectiveness analysis, the cost per life year gained (incremental cost-effectiveness ratio, ICER) is calculated as incremental mean life years divided by mean total cost. For the subsequent line of therapy after daratumumab, the mean life years and mean total cost were 0.567 and $35,601, respectively. The incremental mean life years for a new drug would be (L–0.567), and the incremental mean total cost would be (C–35,601). Therefore, ICER=(C–35,601)/(L–0.567). To estimate the mean total cost of the new drug, the equation can be rearranged as C (in $)=ICER×L–12,245 (in $). Considering the ICER of $84,385 observed in the cost-effectiveness analysis of daratumumab monotherapy, we assume that the new drug should achieve at least an ICER of $84,385. Therefore, C (in $) ≥ 84,385×L–12,245 (in $). By determining the value of L based on the characteristics of the new drug, this formula provides a basis for estimating the minimum medical cost. As shown in the f igure, for expected mean life years of 0.7, 0.8, and 0.9, the minimum medical costs are $46,824, $55,263, and $63,701, respectively (dashed line). Note: Mean life years (L) is calculated using a semi-Markov multi-state modeling approach with the Weibull distribution.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Characteristic	Overall (n=974)	Case cohort (n=273)	Control cohort (n=701)	p-value
Sex
Male	528 (54.2)	132 (48.4)	396 (56.5)	0.03
Female	446 (45.8)	141 (51.6)	305 (43.5)
Age (yr)
19-59	374 (38.4)	99 (36.3)	275 (39.2)	0.37
60-69	370 (38.0)	99 (36.3)	271 (38.7)
70-79	208 (21.4)	68 (24.9)	140 (20.0)
≥ 80	22 (2.3)	7 (2.6)	15 (2.1)
Year at diagnosis of multiple myeloma
2010	73 (7.5)	4 (1.5)	69 (9.8)	< 0.001
2011	70 (7.2)	8 (2.9)	62 (8.8)
2012	125 (12.8)	19 (7.0)	106 (15.1)
2013	168 (17.2)	24 (8.8)	144 (20.5)
2014	141 (14.5)	32 (11.7)	109 (15.5)
2015	116 (11.9)	35 (12.8)	81 (11.6)
2016	123 (12.6)	51 (18.7)	72 (10.3)
2017	83 (8.5)	47 (17.2)	36 (5.1)
2018	49 (5.0)	37 (13.6)	12 (1.7)
2019	26 (2.7)	16 (5.9)	10 (1.4)
Index of line of therapy
4	476 (48.9)	133 (48.7)	343 (48.9)	0.20
5	296 (30.4)	92 (33.7)	204 (29.1)
6	202 (20.7)	48 (17.6)	154 (22.0)
Diagnosis to index date (yr)	4.13±1.99	4.11±2.07	4.14±1.96	0.85
Total medical cost ($)	12,498.0±11,122.2	15,333.1±11,973.4	11,393.8±10,577.6	< 0.001
Charlson Comorbidity Index score
0 to 1	302 (31.0)	88 (32.2)	214 (30.5)	0.71
2	237 (24.3)	69 (25.3)	168 (24.0)
3	160 (16.4)	39 (14.3)	121 (17.3)
4 or higher	275 (28.2)	77 (28.2)	198 (28.2)
Comorbidities at index date
Myocardial infarction	11 (1.1)	2 (0.7)	9 (1.3)	0.69
Congestive heart failure	109 (11.2)	32 (11.7)	77 (11.0)	0.83
Peripheral vascular disease	96 (9.9)	32 (11.7)	64 (9.1)	0.27
Cerebrovascular disease	96 (9.9)	25 (9.2)	71 (10.1)	0.73
Dementia	18 (1.8)	6 (2.2)	12 (1.7)	0.81
Chronic pulmonary disease	520 (53.4)	144 (52.7)	376 (53.6)	0.86
Connective tissue disease	34 (3.5)	11 (4.0)	23 (3.3)	0.71
Peptic ulcer disease	314 (32.2)	81 (29.7)	233 (33.2)	0.32
Mild liver disease	229 (23.5)	67 (24.5)	162 (23.1)	0.70
Diabetes without chronic complication	311 (31.9)	87 (31.9)	224 (32.0)	0.99
Diabetes with chronic complication	101 (10.4)	23 (8.4)	78 (11.1)	0.26
Hemiplegia or paraplegia	18 (1.8)	3 (1.1)	15 (2.1)	0.41
Renal disease	132 (13.6)	43 (15.8)	89 (12.7)	0.25
Any malignancy, including lymphoma and leukemia, except malignant neoplasm of skin	142 (14.6)	38 (13.9)	104 (14.8)	0.79
Moderate or severe liver disease	3 (0.3)	0	3 (0.4)	0.66
Metastatic solid tumor	11 (1.1)	1 (0.4)	10 (1.4)	0.29
AIDS/HIV	1 (0.1)	1 (0.4)	0	0.63

	Daratumumab monotherapy	Standard of care	Incremental
Mean life years	0.986	0.794	0.192
Mean total cost ($)	57,176	40,987	16,189
Cost per life year gained (ICER, $)			84,385^a)

Table 1. Baseline characteristics of patients treated with case cohort (daratumumab monotherapy) and control cohort (SOC)

Values are presented as number (%) or mean±SD. AIDS/HIV, acquired immune deficiency syndrome/human immunodeficiency virus; SD, standard deviation; SOC, standard of care.

Table 2. Cost-effectiveness analysis

The incremental cost-effectiveness ratio (ICER) is $84,385, which is calculated by dividing the incremental cost ($16,189) by the incremental life years (0.192).