Cost-Effectiveness of Tarlatamab in the Second-Line Treatment of Refractory Small Cell Lung Cancer From a US Perspective

Abstract

Background:

Compared with chemotherapy, tarlatamab significantly prolonged overall survival in patients with extensive-stage small cell lung cancer (ES-SCLC) whose disease progressed during or after platinum-based chemotherapy. The aim of this study was to evaluate the cost-effectiveness of tarlatamab versus chemotherapy as a second-line treatment for ES-SCLC from the perspective of the US health care system.

Methods:

A partitioned survival model was constructed to simulate disease progression on the basis of the DeLLphi-304 trial results. A 28-day cycle length and a 10-year time horizon were adopted for the model. Direct medical costs and health utility estimates were extracted from previously published studies and publicly available databases. The model outputs included the total and incremental costs and quality-adjusted life years (QALYs). The primary outcome was the incremental cost-effectiveness ratio (ICER). The willingness-to-pay (WTP) thresholds were set at $150 000/QALY and $200 000/QALY for the United States. One-way sensitivity analysis and probabilistic sensitivity analyses were performed to evaluate the robustness of the model outcomes.

Results:

At an incremental cost of $203 332.28, tarlatamab yielded an additional 0.29 QALYs compared with chemotherapy. This resulted in an ICER of $701 145.79/QALY, which substantially exceeded the WTP thresholds. The cost of tarlatamab emerged as a major influential parameter in the sensitivity analyses, demonstrating its substantial impact on cost-effectiveness outcomes. Sensitivity and scenario analyses confirmed the robustness of the cost-effectiveness results.

Conclusion:

At WTP thresholds of $150 000/QALY and $200 000/QALY, tarlatamab was not considered a cost-effective option at the current price compared with chemotherapy for the treatment of recurrent ESCLC from the US payer perspective.

Keywords

Cost-effectiveness analysis small cell lung cancer tarlatamab chemotherapy second-line treatment

Introduction

Lung cancer, which has the highest incidence and mortality rates among all cancer types across populations, represents a critical public health emergency worldwide.¹ Small cell lung cancer (SCLC) accounts for 10% to 15% of lung cancer cases and is highly aggressive, with most patients diagnosed at an extensive stage.^2,3 Historically, the standard first-line treatment for patients with extensive-stage small cell lung cancer (ES-SCLC) has primarily involved platinum-based agents combined with etoposide, resulting in a 5-year overall survival (OS) rate of less than 5%.⁴

The widespread use of immune checkpoint inhibitors has led to breakthroughs in ES-SCLC treatment. Atezolizumab and durvalumab have increased the 5-year OS rate to approximately 12% in patients with ES-SCLC.^5,6 However, most patients who received first-line treatment experience disease recurrence and progression within 2 years. Notably, patients with a chemotherapy-free interval of less than 90 days following initial therapy had significantly worse prognostic outcomes.^7,8 Moreover, resistance to first-line therapies significantly limits the availability of effective second-line treatment options, posing significant challenges in the management of relapsed SCLC. Traditionally, chemotherapeutic agents such as topotecan or amrubicin constitute the main second-line treatment for SCLC, but the median OS rarely exceeds 8 months.⁹ Therefore, research efforts are increasingly focused on the development of innovative treatment strategies for this aggressive cancer.

Tarlatamab has emerged as a pivotal therapeutic option for the second-line treatment of patients with ES-SCLC. In 2024, the Food and Drug Administration granted accelerated approval for the use of tarlatamab to treat ES-SCLC that progresses during or after platinum-based chemotherapy regimens.¹⁰ DeLLphi-304, a multinational phase 3 trial, demonstrated that tarlatamab significantly prolonged OS compared with chemotherapy (median 13.6 months [95% confidence interval, CI: 11.1 to not reached] vs 8.3 months [95% CI, 7.0 to 10.2]; stratified hazard ratio for death, 0.60; 95% CI, 0.47 to 0.77; P < .001).¹¹ As the first bispecific T-cell engager molecule developed to target delta-like ligand 3 (DLL3), tarlatamab was designed to stimulate the immune system of the patient to target and eliminate DLL3-expressing tumor cells.¹² However, with its high treatment cost and potential for causing immune-related adverse reactions, including cytokine release syndrome, a systematic pharmacoeconomic evaluation of this novel drug is important and warranted. Such evaluations consider the clinical value of any drug and the balance between treatment costs and patient benefits, thereby providing a robust scientific basis for the rational use of drugs in clinical practice and for making informed health policy decisions.

Therefore, the aim of this study was to evaluate the cost-effectiveness of tarlatamab versus chemotherapy as a second-line treatment for SCLC from the perspective of the US health care system.

Methods

This study was reported according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist (Supplemental Table 1).¹³

Clinical data sources

The target population in the DeLLphi-304 trial comprises adults (⩾ 18 years) with SCLC who have developed progressive disease following first-line platinum-based chemotherapy. In total, 509 patients were randomized in a 1:1 ratio to receive either tarlatamab or chemotherapy. The tarlatamab regimen was as follows: 1 mg on day 1; 10 mg on days 8 and 15; and 10 mg every 2 weeks in 28-day cycles. The available chemotherapy options were topotecan (1.5 mg/m² intravenously or 2.3 mg/m² orally on days 1-5 every 3 weeks) and lurbinectedin (3.2 mg/m² intravenously on day 1 every 3 weeks). Amrubicin was excluded because it is not approved in the United States. In the DeLLphi-304 trial, 185 patients received topotecan, and 47 patients received lurbinectedin. As the DeLLphi-304 trial did not provide information on the proportion of patients receiving intravenous versus oral topotecan, an equal distribution was assumed between the 2 groups. Patients who received tarlatamab had a median treatment duration of 4.2 months, whereas those receiving chemotherapy had a median duration of 2.5 months. Following disease progression, 112 patients in the tarlatamab group and 125 patients in the chemotherapy group received subsequent antitumor therapy. Owing to the lack of a standard third-line treatment for SCLC, these patients were assumed to have received either nivolumab or pembrolizumab. Those who did not receive further anticancer therapy were considered to have received the best supportive care (Supplemental Table 2).^14,15

Model construction

TreeAge Pro 2022 software (Williamstown, MA, USA) was used to construct a partitioned survival model for simulating disease progression, with 3 distinct health states included: progression-free survival (PFS), progressive disease (PD), and death. A 28-day cycle length and a 10-year time horizon were used for the model. All patients were assumed to enter the model in the PFS state, with transitions occurring between mutually exclusive health states at each cycle receiving corresponding interventions according to their current state. The WebPlot Digitizer was used to estimate the proportion of patients in different health states on the basis of Kaplan-Meier survival curves generated from the clinical trial. Individual patient data were subsequently reconstructed using the survHE package in R software (version 4.2.3, Vienna, Austria).^11,16 The best-fitting distribution model was identified by means of a systematic evaluation using the Akaike information criterion (AIC), Bayesian information criterion (BIC), and visual assessments. Lower AIC and BIC values, combined with a robust visual fit, indicated the superior performance of the model. Log-normal distribution was used to extrapolate the PFS data for both treatment groups. The tarlatamab group was modeled using an exponential distribution for the OS curves, whereas the chemotherapy group was modeled using a log-logistic distribution (Figure 1 and Supplemental Table 3).

Figure 1.

Kaplan-Meier curves of PFS and OS fitting and extrapolation. (A) PFS in the tarlatamab group. (B) PFS in the chemotherapy group. (C) OS in the tarlatamab group. (D) OS in the chemotherapy group. OS, overall survival; PFS, progression-free survival.

The main outcome measure of this study was the incremental cost-effectiveness ratio (ICER). The output of the model included quality-adjusted life years (QALYs), incremental QALYs, total costs, and incremental costs. The costs and QALYs were discounted at an annual rate of 3%.¹⁷ The willingness-to-pay (WTP) thresholds were set at $150 000/QALY and $200 000/QALY for the United States.¹⁸

Costs and utilities

In this study, direct medical costs, including expenditures associated with medications, follow-up treatments, best supportive care, management of adverse events (AEs), end-of-life care, and other health care–related services, were exclusively considered from the perspective of the US payers. Drug costs were obtained from the October 2025 Medicare Part B drug average sales price listed by the Centers for Medicare & Medicaid Services and the average price listed on the Drugs.com price guide (https://www.drugs.com/price-guide/).¹⁹ An average body weight of 77.5 kg and a body surface area of 1.86 m² were assumed for the target population to accurately calculate drug dosing and drug costs.²⁰ Other costs were sourced from published literature and adjusted to 2025 price levels using the consumer price index.²¹ In this study, only grade 3/4 AEs with higher than 5% incidence in the clinical trial were included. The costs of these AEs were derived from published literature and considered a one-time expenditure.^21-25 Owing to the lack of related data from the DeLLphi-304 trial, the health utility values used in this analysis were obtained from published literature. These utility values were based on EQ-5D-5L assessments, with values of 0.70 for the PFS state and 0.60 for the PD state.^26,27 Adverse events result in a decrease in the health utility measures of patients. The model incorporated disutility values associated with AEs were based on previously published studies.^28,29 The detailed information regarding costs and utilities is presented in Table 1.

Table 1.

Model parameters.

Parameter	Value	Range		Reference
Parameter	Value	Minimum	Maximum	Reference
Clinical data
Survival model for tarlatamab
Exponential model for OS	Rate = 0.0487			Model fitting
Lognormal model for PFS	Meanlog = 1.414Sdlog = 1.113			Model fitting
Survival model for chemotherapy
Loglogistic model for OS	Shape = 1.507Scale = 8.653			Model fitting
Lognormal model for PFS	Meanlog = 1.202Sdlog = 0.843			Model fitting
Cost data ($)
Drug cost per mg
Tarlatamab	1611.28	1289.02	1933.54	Drug. price guide
Topotecan (injection)	747.79	598.23	897.35	Drug. price guide
Topotecan (oral)	519.275	415.42	623.13	Drug. price guide
Lurbinectedin	2071.27	1657.02	2485.52	CMS
Nivolumab	139.55	111.64	167.46	Drug. price guide
Pembrolizumab	252.24	201.79	302.69	Drug. price guide
Best support care per cycle	2207.21	1765.77	2648.65	21
Follow-up per cycle	367.41	293.93	440.89	21
End-of-life, one time	32 934.53	26 347.62	39 521.44	21
Cost of severe AEs, $
Hyponatremia	0.29	0.23	0.35	25
Pneumonia	28 440.28	22 752.22	34 128.34	24
Fatigue	10 229.69	8183.75	12 275.63	26
Neutrophil count decrease	15 614	12 491.20	18 736.80	21
Platelet count decrease	14 984	11 987.20	17 980.80	21
Febrile neutropenia	29 315	23 452.00	35 178.00	23
Thrombocytopenia	14 984	11 987.20	17 980.80	21
Leukopenia	14 984	11 987.20	17 980.80	21
Neutropenia	15 614	12 491.20	18 736.80	21
Anemia	9079.75	7263.80	10 895.70	21
Incidence of AEs
Tarlatamab group
Hyponatremia	5%	4.00%	6.00%	11
Pneumonia	6%	4.80%	7.20%	11
Neutropenia	6%	4.80%	7.20%	11
Chemotherapy group
Hyponatremia	5%	4.00%	6.00%	11
Pneumonia	8%	6.40%	9.60%	11
Fatigue	7%	5.60%	8.40%	11
Neutrophil count decrease	11%	8.80%	13.20%	11
Platelet count decrease	8%	6.40%	9.60%	11
Febrile neutropenia	11%	8.80%	13.20%	11
Thrombocytopenia	11%	8.80%	13.20%	11
Leukopenia	14%	11.20%	16.80%	11
Neutropenia	23%	18.40%	27.60%	11
Anemia	29%	23.20%	34.80%	11
Utility
PFS	0.7	0.56	0.84	27
PD	0.6	0.48	0.72	28
Disutility
Hyponatremia	−0.09			29
Pneumonia	−0.07			30
Fatigue	−0.07			30
Neutrophil count decrease	−0.2			30
Platelet count decrease	−0.05			30
Febrile neutropenia	−0.2			30
Thrombocytopenia	−0.2			30
Leukopenia	−0.2			30
Neutropenia	−0.09			30
Anemia	−0.07			30

Abbreviations: AEs, adverse events; OS, overall survival; PD, progression; PFS, progression-free survival.

Sensitivity and Scenario Analyses

One-way sensitivity analysis and probabilistic sensitivity analyses were conducted to evaluate the robustness of the model outcomes. In the one-way sensitivity analysis, each parameter was adjusted to its maximum or minimum value within a 20% range around the base case to assess its impact on the base-case outcomes. Tornado diagrams were used to illustrate the outcomes and identify the most influential parameters. For the probabilistic sensitivity analysis, a Monte Carlo simulation with 10 000 iterations was performed, with all the parameters being simultaneously sampled from their respective probability distributions. Cost-effectiveness acceptability curves were used to summarize the results and show the probability of the intervention being cost-effective at various WTP thresholds. To evaluate the robustness of the extrapolations, scenario analyses were performed by fitting the survival curves with various parametric distributions (exponential, Weibull, Gompertz, log-logistic, and log-normal). In addition, the impacts of 2 alternative third-line treatment strategies were evaluated: (A) best supportive care alone and (B) excluding subsequent treatment costs from the study outcomes.

Results

Base-case results

The results of the base-case results are summarized in Table 2. Tarlatamab incurred a total cost of $392 812.09 and utility of 1.08 QALYs, whereas the values for chemotherapy were $189 479.81 and 0.79 QALYs, respectively. Therefore, compared with chemotherapy, tarlatamab resulted in an incremental cost of $203 332.28 and yielded an additional 0.29 QALYs. Consequently, the ICER was $701 145.79/QALY, which exceeds the WTP thresholds of $150 000/QALY and $200 000/QALY in the United States.

Table 2.

Results of base-case analysis.

Regimen	Cost ($)	Effectiveness (QALYs)	ICER ($/QALY)
Tarlatamab	392 812.09	1.08	701,145.79
Chemotherapy	189 479.81	0.79	-

Abbreviations: ICER, incremental cost-effectiveness ratio; QALYs, quality-adjusted life years.

Sensitivity analysis

The results of the one-way sensitivity analysis are presented in Figure 2. As demonstrated in Figure 2, the parameters with a dominant influence on the ICER are the cost of tarlatamab and the utilities for the PFS state, whereas follow-up costs have a limited impact on the results. Even when all the parameters vary within the predefined ranges of this study, the core conclusions remain unaffected, demonstrating the robustness of the findings. According to the cost-effectiveness acceptability curve (Figure 3), tarlatamab has a 0% probability of being cost-effective at a WTP threshold of $150 000/QALY. With increase in the WTP threshold, the probability of tarlatamab being cost-effective also increased. The results of the scenario analyses for curve fitting are shown in Supplemental Table 4. The Gompertz function was not adopted because the generated PFS and OS curves exhibited crossover, which is inconsistent with the clinical setting. The lognormal function yielded the most favorable ICER, whereas the Weibull function resulted in the highest value. Two alternative third-line strategies were evaluated in 2 scenarios: (A) best supportive care alone and (B) standard therapy. Supplemental Table 5 shows that adjusting the costs of subsequent treatment increased the ICER to $744 498.14/QALY for scenario A and $733 938.48/QALY for scenario B. Despite this increase, the results do not alter the conclusions of the base-case analyses.

Figure 2.

Tornado diagram for the sensitivity analysis. This diagram presents the results of the sensitivity analysis comparing tarlatamab with chemotherapy. The influential parameters are listed in descending order of their impact on the results. AEs, adverse events; OS, overall survival; PD, progression; PFS, progression-free survival.

Figure 3.

Cost-effectiveness acceptability curves for tarlatamab versus chemotherapy. The cost-effectiveness acceptability frontier shows the probability of each strategy being cost-effective across a range of willingness-to-pay thresholds.

Discussion

The prognosis of patients with recurrent SCLC is generally poor, particularly among those with platinum-resistant disease, which is defined as having a treatment-free interval of less than 90 days following initial platinum-based chemotherapy.^30,31 Therefore, SCLC recurrence remains a significant challenge in clinical practice. In recent years, immunotherapy has garnered considerable attention as an approach. DLL3 is a novel therapeutic target owing to its widespread and specific expression in SCLC cells. As a DLL3-targeting immune agent, tarlatamab has resulted in promising survival outcomes in patients with SCLC who experienced disease progression during or after platinum-based chemotherapy. However, the substantial cost barriers render value assessment a key determinant in the clinical decisions of both providers and patients.

Our study is the first to evaluate the cost-effectiveness of tarlatamab versus conventional chemotherapy for the second-line treatment of SCLC. Compared with chemotherapy, tarlatamab yielded an incremental gain of 0.29 QALYs at a cumulative cost of $203 332.28, resulting in an ICER of $701 145.79/QALY. This ICER was significantly higher than the WTP threshold assumed in our study. Sensitivity analysis revealed that the cost of tarlatamab was the primary determinant of the model outcomes. In the United States, measures including drug price negotiations, rebates for price increases, and expanded prescription drug subsidies have been implemented.³² These policy interventions may enhance the cost-effectiveness of innovations and accelerate drug access for patients. Based on our model, achieving cost-effectiveness at a $200 000/QALY threshold would require an approximately 56% price reduction compared with standard chemotherapy. However, the limited availability of effective second-line therapies for SCLC patients may justify a higher WTP threshold in certain market or health care financing contexts. Future research should quantify this effect and identify when unmet needs appropriately influence economic valuations in oncology.

Historically, given the limited therapeutic options for SCLC, pharmacoeconomic evaluations across different regions have predominantly focused on conventional chemotherapy regimens, revealing that chemotherapy imposes a substantial economic burden on patients.^33-35 However, in recent years, several studies have assessed the economic profiles of novel agents for recurrent SCLC. From the perspective of the health care system in China, anlotinib has been identified as the most cost-effective option for the third-line treatment of recurrent SCLC compared with pembrolizumab, nivolumab, or a placebo.^36,37 Conversely, Smare et al³⁸ reported that at a WTP threshold of $250,000/QALY, nivolumab monotherapy represents a cost-effective alternative to both intravenous and oral topotecan as a third-line treatment for SCLC in the United States.

Our study had several limitations. First, the clinical data used in this study were obtained from the DeLLphi-304 trial. Given the limited follow-up duration and lack of available individual patient data, the parametric survival modeling used to extrapolate the OS and PFS curves may introduce potential uncertainty. Second, only the costs of AEs of grade 3 or higher with an incidence of more than 5% were calculated, which could deviate from real-world safety profiles. However, the one-way sensitivity analysis confirmed that the cost of AEs management has a minimal effect on the economic conclusions of the model. Third, when drug prices were unavailable from the Center for Medicare & Medicaid Services, data were obtained from the Drug.com price guide. These values represent listed or retail prices, which may overestimate the actual acquisition cost. Fourth, because there are no publicly available utility data for patients receiving tarlatamab for SCLC treatment, the utility values were derived from previous studies. A deterministic sensitivity analysis confirmed that altering these utility values by ±20% did not affect the conclusions of the study. Finally, we assumed that the third-line therapy for SCLC in our study may not reflect the heterogeneous treatment patterns observed in clinical practice.

Conclusions

The results of this study indicate that from the perspective of a health care payer in the United States and at the WTP thresholds of $150 000/QALY and $200 000/QALY, tarlatamab at its current price is not cost-effective compared with standard chemotherapy for the treatment of SCLC after platinum-based chemotherapy. Although this study provides a reference for clinical decision-making regarding ES-SCLC, it has several limitations. Nonetheless, the limited therapeutic options and urgent clinical needs in the second-line treatment setting for SCLC could justify accepting a higher WTP in specific contexts. In future research studies, real-world treatment patterns and disease-specific WTP thresholds should be explored, the elucidation of which will provide more comprehensive guidance for clinical decision-making and health policy formulation.

Supplemental Material

sj-docx-1-onc-10.1177_11795549261441335 – Supplemental material for Cost-Effectiveness of Tarlatamab in the Second-Line Treatment of Refractory Small Cell Lung Cancer From a US Perspective

Supplemental material, sj-docx-1-onc-10.1177_11795549261441335 for Cost-Effectiveness of Tarlatamab in the Second-Line Treatment of Refractory Small Cell Lung Cancer From a US Perspective by Hanrui Zheng, Feng Wen and Bin Wu in Clinical Medicine Insights: Oncology

Footnotes

Acknowledgements

The research was supported by the National Key Clinical Specialties Construction Program.

ORCID iD

Hanrui Zheng

Ethical Considerations

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Therefore, ethical approval and informed consent to participate were not required.

Author Contributions

Hanrui Zheng: Data curation; Formal analysis; Software; Writing – original draft.

Feng Wen: Conceptualization; Methodology; Validation; Writing – review & editing.

Bin Wu: Conceptualization; Resources; Supervision; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data is provided within the manuscript.

Supplemental Material

Supplemental material for this article is available online.

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