Cost-effectiveness analysis of tepotinib vs capmatinib as subsequent therapy in MET exon 14-mutated non-small-cell lung cancer

Abstract

Background

This study evaluated the cost-effectiveness of Tepotinib versus Capmatinib in patients with advanced or metastatic non-small cell lung cancer (NSCLC) with MET Exon 14 Skipping Mutations in China.

Methods

An economic evaluation using a 3-state partitioned survival model assessed the cost-effectiveness of Tepotinib versus Capmatinib. The Kaplan-Meier (KM) curves for overall survival (OS) and progression-free survival (PFS) from two clinical trials were digitally extracted. The Exponential model with matching-adjusted indirect comparison (MAIC) was employed at the end of the trials to extrapolate the long-term survivals.

Results

The estimated cost and utility of Tepotinib treatment were higher than those of Capmatinib treatment, respectively (95,392.54 USD vs. 51,003.63 USD; 2.11 QALYs vs 1.38 QALYs). The incremental cost-effectiveness ratio (ICER) of Capmatinib treatment vs. Tepotinib treatment was calculated at 60,977.28 USD/QALY.

Conclusions

Tepotinib was not cost-effective compared to Capmatinib as the second-line treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations in China.

PLAIN LANGUAGE SUMMARY

Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) represented approximately 80–85% of lung cancer cases, and the majority of those were diagnosed in advanced stages. The National Comprehensive Cancer Network (NCCN) guidelines (NSCLC, version 3, 2025) recommend testing specific biomarkers to select the most appropriate treatment strategy for those patients harboring oncogenic alterations, including mutation of the mesenchymal-epithelial transition (MET). Currently, two selective MET tyrosine kinase inhibitors, Capmatinib and Tepotinib, have been demonstrated to be highly effective in this molecularly defined subgroup of patients. However, there were few reports of their economic evaluation as the second-line treatment of metastatic NSCLC. This study would apply a cost-effectiveness analysis to conduct an economic evaluation of Tepotinib versus Capmatinib in advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation. An economic evaluation using a 3-state partitioned survival model assessed the cost-effectiveness of Tepotinib versus Capmatinib in our study. The Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) from two clinical trials were digitally extracted. The Exponential model and the Weibull model were employed at the end of the trials to extrapolate the long-term survivals. Our study found that Tepotinib was not cost-effective compared to Capmatinib as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations in China.

ARTICLE HIGHLIGHTS

1. A partitioned survival model was developed to assess the cost-effectiveness of Tepotinib versus Capmatinib as the second-line treatment of metastatic NSCLC from the perspective of healthcare payers in China.

2. Both the estimated cost and utility of Tepotinib treatment were higher than those of Capmatinib treatment as the second-line treatment for metastatic NSCLC patients with MET exon 14 skipping mutations.

3. Tepotinib was not cost-effective compared to Capmatinib as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations in China.

GRAPHICAL ABSTRACT

1. Introduction

Lung cancer was established as the second most common cancer worldwide (the most common cancer in men and the second most common cancer in women) and as the leading cause of cancer morbidity among neoplasms [1]. Non-small cell lung cancer (NSCLC) represented approximately 80–85% of lung cancer cases, and the majority of those were diagnosed in advanced stages (IIIB/C or IV) [2]. Most of the patients also developed metastases and demonstrated disease relapse even after a complete resection of the tumor [3]. In China, lung cancer is a primary cancer type with high incidence and mortality. Risk factors for lung cancer included tobacco use, family history, radiation exposure, and the presence of chronic lung diseases. Most early-stage NSCLC patients missed the optimal timing for treatment due to the lack of clinical presentations [4]. Over the past few years, there has been an upward trend in lung cancer incidence and mortality in China. The 2020 global cancer statistics reported by the International Agency for Research on Cancer revealed that an estimated 820,000 new lung cancer diagnoses and 715,000 lung cancer-related deaths occurred in China in 2020 [5]. Therefore, the choice of treatment was of great significance for improving the quality of life and reducing the economic burden of NSCLC patients.

The National Comprehensive Cancer Network (NCCN) guidelines (NSCLC, version 3, 2025) recommend testing specific biomarkers to select the most appropriate first-line treatment strategy for those patients harboring oncogenic alterations, including mutations of the epidermal growth factor receptor (EGFR) and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) or rearrangements of anaplastic lymphoma kinase (ALK) or c-ros oncogene 1 (ROS1) [6]. For patients without driver alterations and high programmed cell death-ligand 1 (PD-L1) expression [tumor proportion score (TPS) ≥ 50%], immunotherapy has become the standard of care, while a combination of chemotherapy and immunotherapy was recommended for most patients with negative or low PD-L1 expression [7]. Recently, emerging biomarkers, including genetic alterations of the Kirsten rat sarcoma viral oncogene homolog (KRAS), the human epidermal growth factor receptor 2 (HER2), the mesenchymal-epithelial transition (MET) and rearrangements of the neurotrophic tyrosine receptor kinase (NTRK) and the rearranged during transfection (RET) genes, had gained significant attention because of the development of selective inhibitors, some of which had already received approval for clinical use in pretreated NSCLC patients [8]. MET-directed therapies, including tyrosine kinase inhibitors (TKIs) and monoclonal antibodies targeting MET, MET ligands, or the hepatocyte growth factor (HGF), had been tested in patients with advanced NSCLC with MET deregulation, mainly due to exon 14 skipping mutations (METex14) or MET amplification (MET-amp). Currently, two selective MET TKIs, Capmatinib and Tepotinib, have been demonstrated to be highly effective in this molecularly defined subgroup of patients, as shown in the GEOMETRY Mono-1 and VISION trials, respectively, and have obtained FDA and EMA approval [9]. In addition, there were some reports showing that Capmatinib could significantly exert the anti-tumor effect through the nanoparticles, as the nanoparticles had the potential value of treating tumors in the future [10–14]. According to the NCCN guideline (NSCLC, version 3, 2025), the first-line therapy or subsequent therapy regimen for advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation was Capmatinib or Tepotinib [6]. Therefore, it would be very important to compare the efficacy of Capmatinib and Tepotinib in the treatment of NSCLC.

Decision makers and patients required more information to make reasonable decisions regarding cancer treatment, and cost represented a crucial factor. In recent years, expenditures on cancer care have increased, with cancer becoming an important health concern worldwide, especially in countries such as China, which have limited healthcare resources. Thus, the evaluation of the pharmacoeconomic profile of treatment regimens was increasingly crucial [15]. Cost-effectiveness analysis was a principal tool in health economic evaluation that allowed for the analysis of both the cost of a specific medical intervention and the benefits that it provided [16]. Recently, two clinical trials reported the efficacy and safety of Capmatinib and Tepotinib as a second-line or subsequent treatment of advanced or metastatic NSCLC, respectively [17,18]. However, there were few reports of their economic evaluation in the treatment of NSCLC. This study would apply a cost-effectiveness analysis to conduct an economic evaluation of Tepotinib versus Capmatinib in advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation.

2. Methods and materials

2.1. Target population and treatment strategies

Our study adhered to the provisions of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) [19]. The source of efficacy and safety data of Tepotinib and Capmatinib was from two global, multicenter, open-label, phase 2 trials (VISION and GEOMETRY) [17,18]. The ongoing VISION study was being conducted at more than 130 sites in 11 countries. Patients with MET exon 14 skipping mutations were enrolled to assess the anti-tumor activity and side-effect profile of 500 mg of Tepotinib given orally once daily until disease progression, consent withdrawal, or adverse events leading to discontinuation. Patients were 18 years of age or older with histologically or cytologically confirmed, locally advanced or metastatic NSCLC with MET exon 14 skipping mutations. All the patients had measurable disease according to the Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1, and a performance status of 0 or 1 on the Eastern Cooperative Oncology Group scale (ECOG). In addition, all the patients had negative results on local testing for the presence of EGFR mutations or ALK rearrangements. Patients could have received up to two courses of previous treatment for advanced or metastatic disease [17]. The GEOMETRY study evaluated capmatinib in patients with advanced NSCLC with METex14 or MET amplification, which was conducted in 152 centers and hospitals in 25 countries. Eligible patients were aged 18 years or older with advanced or metastatic (stage IIIB/IV), dysregulated MET, EGFR wild-type, and ALK rearrangement-negative NSCLC, with an ECOG performance status score of 0 or 1 and at least one measurable lesion according to the RECIST, version 1.1. The GEOMETRY study included first-line and second-line or third-line patients [18]. Capmatinib was administered orally at a dose of 400 mg twice daily continuously in the treatment cycles. Treatment continued until patients had progressive disease as determined by the investigator and confirmed by a blinded independent review committee, or had unacceptable toxicity, were lost to follow-up, or died.

2.2. Disease modeling

A partitioned survival model (PSM) was employed for this analysis. A PSM was a commonly used model in the economic evaluation of oncology drugs. The PSM used the area under the curve to represent the number of patients in each state. It was mainly used to evaluate the impact of interventions that could prolong the life of the patient on the expected lifetime and quality of life of the patient [20]. Three health states were used by TreeAge Pro Healthcare 2022 software to frame the PSM in our study, including progression-free survival (PFS), progression disease (PD), and death (Figure 1). Engauge Digitizer was applied to extract and digitize the parametric survival curves reported in the studies to simulate long-term survival of advanced or metastatic NSCLC patients with MET exon 14 skipping mutations. Seven distinct survival models were employed for the survival extrapolation from PFS and OS curves, such as Exponential, Gamma, Gompertz, Weibull (AFT), Weibull (PH), Log-Logistic, and Log-Normal models. Specifically, deducing the potential survival distribution from the Kaplan-Meier curves (KM) was advantageous for determining the parameters of these models [21]. Selecting the most suitable survival model was based on the evaluation of all the fitted curves through a visualized approach, the lowest Bayesian information criterion value (BIC), and the lowest Akaike information criterion value (AIC). The Exponential model was chosen due to its goodness-of-fit compared to the other models, and many pharmacoeconomic studies had adopted this model [22,23] (Figure 2). Since the Exponential model indicated that the events occurred at a constant hazard rate, approximating the characteristics of the tail of the Kaplan-Meier curve proved difficult [24]. As the Weibull model was also the most commonly used for survival data, we conducted a scenario analysis applying the Weibull model [25]. Matching-adjusted indirect comparison (MAIC) was a pairwise indirect comparison method intended to provide a more accurate comparison of trial data by compensating for between-trial differences in patient characteristics. We referred to the data of the indirect comparison of Tepotinib vs Capmatinib in the treatment of NSCLC, and combined with the survival model for this cost-effectiveness analysis [26]. In the MAIC study, the matching variables selected to weight data from VISION patients for the MAIC were those identified by the Cox regression analysis to have a significant association with OS: age (median), race, ECOG, histology, and smoking history. The results showed that Tepotinib appeared to be associated with prolonged PFS and OS compared with Capmatinib in previously treated patients (PFS HR 0.54; 95%CI 0.36–0.83; OS HR 0.66; 95%CI 0.42–1.06). The KM curves for the Tepotinib populations without MAIC and those with MAIC showed a large degree of overlap, and notable separation from the Capmatinib KM curve in favor of Tepotinib [26]. The cycle length of our model was one month and a half-cycle correction was applied. As it was reported that the survival rates at the 5-year and 10-year time horizons of the NSCLC patients treated with TKIs were 21% and 7%, respectively, we selected the 10-year time horizon to indicate the entire life cycle of a patient in the base case analysis and conducted the subgroup analysis with a 5-year time horizon [27] (Figure 3).

Figure 1.

Model structure of a decision tree and a diagram of the partitioned survival model structure. Abbreviations: NSCLC: Non-Small Cell Lung Cancer; PFS: Progression-Free Survival; PD: Progressive Disease.

Figure 2.

Bayesian information criterion value in different models to select the most suitable survival model. (A) Seven models for OS in the Tepotinib arm; (B) Seven models for OS in the Capmatinib arm; (C) Seven models for PFS in the Tepotinib arm; (D) Seven models for PFS in the Capmatinib arm. Abbreviations: BIC: Bayesian information criterion value.

Figure 3.

Reconstructed Kaplan-Meier curves and Exponential model curves. (A) Reconstructed Kaplan-Meier curves of PFS; (B) Reconstructed Kaplan-Meier curves of OS; (C) PFS and OS curves in the Tepotinib treatment; (D) PFS and OS curves in the Capmatinib treatment.

2.3. Costs and utility

From a payer perspective, this model exclusively included the direct expenditure for medical care services, such as drug fees, costs of BSC (Best Supportive Care), disease management costs, management fees for serious adverse events (SAEs), and follow-up costs. The costs of Tepotinib and Capmatinib were from the Heilongjiang province medical security service platform online service hall and the big data service platform for China’s health industry [28]. On the provincial procurement platform, the price of Tepotinib was about 2160 USD per box (60 tablets/box, 225 mg/tablet), and the price of Capmatinib was about 2040 USD per box (120 tablets/box, 200 mg/tablet). Based on the procedures of the clinical trials and the drug instructions, the therapeutic schedules were as follows: Tepotinib was given 450 mg orally once daily, and Capmatinib was given 400 mg twice daily continuously. The treatment cycle was one month, and both drugs needed to be taken until the disease progression [17,18]. In conclusion, the prices of Tepotinib and Capmatinib were about 2160 USD/cycle and 2040 USD/cycle, respectively. The costs of follow-up care, BSC, disease management, and SAEs management were derived from the published literature [29–31]. In our study, adverse drug reactions with an incidence rate exceeding 5% and a grade surpassing or equal to grade 3 were selected to calculate the costs of SAEs management for the Tepotinib arm and Capmatinib arm. Among them, the costs of SAEs involved peripheral edema, dyspnea, fatigue, and increased alanine aminotransferase [17,18]. The health utility index referred to an aggregate indicator of physical health with weights varying from different states [32]. A range from 0 to 1 (0 denoting death and 1 denoting perfect health) was commonly used for the utility scale. Given the absence of quality-of-life statistics from the clinical trial in this study, the utility values for the PFS and PD of advanced or metastatic NSCLC were sourced from the previously published literature [33–37]. In these studies reporting the utility of the PFS and PD states, most of the advanced NSCLC patients received the second-line treatment. The ranges of utility of the PFS and PD states were 0.670–0.804 and 0.321–0.701, respectively [33–37]. We used the average values of these ranges as the utilities of the PFS and PD states of the advanced NSCLC patients receiving the second-line treatment. As the SAEs might decrease the quality of life of the patient, we added the negative utility value of the SAEs to the calculations and hypothesized that SAEs would occur within the first cycle of treatment with the reported probability of occurrence. In addition, to test the uncertainty of the occurrence of the SAEs, we assumed that all SAEs occurred or did not occur during the treatment period to determine their impact on the results in the subgroup analysis. Costs were presented in 2025 US dollars (USD) and inflated according to the Consumer Price Index (CPI) and currency exchange rates published by the People’s Bank of China (CPI: 2024: 0.20%; 1USD = 7.2 CNY). The yearly discount rate in our study was 5% [38] (Tables 1–3). The cost-effectiveness was assessed by calculating the incremental cost-effectiveness ratio (ICER), which measures the ratio between cost increments and quality-adjusted life years (QALYs) increments. The incremental net monetary benefit (INMB) was also employed (INMB = QALYs increments × WTP-cost increments), and if the INMB was greater than 0, the treatment was considered to be cost-effective (Table 4).

Table 1.

Distribution model of survival data.

Items	Distribution
	Exponential	Weibull
OS curve in the Tepotinib treatment	Rate 0.04128	Shape 1.1417 Scale 0.0280
OS curve in the Capmatinib treatment	Rate 0.03709	Shape 0.9726 Scale 0.0408
PFS curve in the Tepotinib treatment	Rate 0.0714	Shape 1.0453 Scale 0.0637
PFS curve in the Capmatinib treatment	Rate 0.07735	Shape 0.8760 Scale 0.1106

Abbreviations: PFS: progression-free survival; OS: Overall survival.

Table 2.

Cost inputs in the PSM.

Items	Data (year)	Country	Therapeutic schedule	Cost per cycle (USD)	Low	High	Distribution	References
Tepotinib	2025	China	500 mg of tepotinib given orally once daily until disease progression	2160.00	1728.00	2592.00	Gamma	[28]
Capmatinib	2025	China	400 mg twice daily continuously in 21-day treatment cycles	2040.00	1632.00	2448.00	Gamma	[28]
BSC	2025	China	/	3006.28	2405.02	3607.54	Gamma	[29]
Follow-up	2025	China	/	542.62	434.10	651.14	Gamma	[29]
Disease management costs	2025	China	/	765.11	612.09	918.13	Gamma	[30]
Peripheral edema	2025	China	/	0.16	0.13	0.19	Gamma	[30]
Dyspnea	2024	China	/	1789.55	1431.64	2147.46	Gamma	[31]
Fatigue	2024	China	/	1185.92	948.74	1423.10	Gamma	[31]
Alanine aminotransferase increased	2025	China	/	0.06	0.05	0.07	Gamma	[30]

Abbreviations: CPI: Consumer Price Index; CNY: Chinese Yuan; USD: United States Dollar.

Table 3.

Other inputs in the PSM.

Items	Value	Low	High	Distribution	Reference
Utility (QALYs)
PFS	0.74	0.67	0.81	Beta	[33–37]
PD	0.54	0.49	0.59	Beta	[33–37]
Peripheral edema	−0.05	−0.05	−0.06	Beta	[30]
Dyspnea	−0.05	−0.05	−0.06	Beta	[31]
Fatigue	−0.07	−0.06	−0.08	Beta	[31]
ALT increased	−0.06	−0.05	−0.07	Beta	[30]
Other
Discounting rate	0.05	0	0.08	Fixed	[38]
SAEs rate in Tepotinib treatment
Peripheral edema	0.07	0.06	0.08	Beta	[17]
SAEs rate in Capmatinib treatment
Peripheral edema	0.17	0.14	0.20	Beta	[18]
Dyspnea	0.07	0.06	0.08	Beta	[18]
Fatigue	0.06	0.05	0.07	Beta	[18]
ALT increased	0.06	0.05	0.07	Beta	[18]

Abbreviations: PFS: progression-free survival; PD: progressive disease; QALYs: quality adjusted life years; ALT: Alanine aminotransferase; SAEs: serious adverse events.

Table 4.

Economic evaluation results.

Different scenario	Cost (USD)	Utility (QALYs)	IC (uSD)	IE (QALYs)	ICER (USD/QALYs)	INMB
Exponential distribution with MAIC at a 10-year time horizon
Tepotinib	95,392.54	2.11	44,388.91	0.73	60,977.28	−15,188.91
Capmatinib	51,003.63	1.38	/	/	/	/
Weibull distribution with MAIC at a 10-year time horizon
Tepotinib	103,789.94	2.16	51,856.15	0.77	67,307.46	−21,056.15
Capmatinib	51,933.79	1.39	/	/	/	/
Exponential distribution with MAIC at a 5-year time horizon
Tepotinib	88,108.83	1.77	37,687.00	0.50	75,877.37	−17,687.00
Capmatinib	50,421.84	1.27	/	/	/	/
All SAEs occurred in both treatment arms
Tepotinib	95,392.69	2.02	41,500.57	0.86	48,142.52	−7,100.57
Capmatinib	53,892.13	1.16	/	/	/	/
None of the SAEs occurred in both treatment arms
Tepotinib	95,392.53	2.12	44,592.33	0.71	62,489.69	−16,192.33
Capmatinib	50,800.21	1.41	/	/	/	/

Abbreviations: PFS: progression-free survival; PD: progressive disease; QALYs: quality adjusted life years; NMB: Incremental net monetary benefit; SAEs: serious adverse events.

2.4. Sensitivity analysis

In this study, both one-way sensitivity analysis and probabilistic sensitivity analysis (PSA) were employed for uncertainty measurement and robustness evaluation of the model. Among them, the costs of Tepotinib and Capmatinib were obtained from the Heilongjiang medical security service platform online service hall and the big data service platform for China’s health industry. Tepotinib and Capmatinib had only two prices on the platform. We adopted the lower prices of Tepotinib and Capmatinib as the main prices in the base-case analysis, and set the higher price as the upper limit of the range. Since much of the literature used a 20% decrease in value as the lower limit of the range, we also used a 20% decrease in the lower prices of Tepotinib and Capmatinib as the lower limit of the range in the sensitive analysis. The ranges of other variables were determined by referring to those reported in other literature (Tables 1–3). A Monte Carlo simulation was performed 10,000 times for PSA, with each iteration randomly sampling from the distributions of all parameters. We conducted the scenario analysis and subgroup analysis to explore the uncertainties of the survival extrapolation, the 5-year time horizon, and the different SAE rates, respectively. To illustrate the uncertainty of potential willingness-to-pay (WTP) thresholds, the cost-effectiveness acceptability curves were employed [39]. The WTP threshold in the cost-effectiveness acceptability curves was set at 40,000 USD/QALY, which was about three times the per capita GDP of China in 2024 from the National Bureau of Statistics [38]. In cost-effectiveness analysis, the WTP threshold value for QALY was recommended to be 1–3 times the GDP per capita [38]. The WTP threshold was defined by the WHO as a value representing an estimate of what a consumer of healthcare might be prepared to pay for the health benefit. These thresholds were often based on cost-effectiveness ratios estimating health gains for the resources expended [40]. The cost parameters were modeled using a gamma distribution, whereas the utility value parameters and SAE rate parameters were modeled with a beta distribution [41].

3. Results

3.1. Base case findings

When the Exponential model with MAIC was applied, the estimated cost of Tepotinib treatment was higher than that of Capmatinib treatment (95,392.54 USD vs. 51,003.63 USD). The estimated utility of Tepotinib treatment was lower than that of Capmatinib treatment (2.11 QALYs vs 1.38 QALYs). The incremental cost-effectiveness ratio (ICER) of Capmatinib treatment vs. Tepotinib treatment was calculated at 60,977.28 USD/QALY (Table 4). Our results suggested the Tepotinib treatment was not cost-effective compared to Capmatinib treatment as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations at a WTP threshold of 40,000 USD/QALY.

3.2. Sensitivity analysis

A tornado diagram was employed to visualize the outcomes of the one-way sensitivity analysis, revealing that the most influential factors on the ICER were the OS HR (Tepotinib vs Capmatinib), the costs of Tepotinib and Capmatinib, and the PFS HR (Tepotinib vs Capmatinib) in the Exponential model with MAIC. The PSA revealed 95.83% possibility that Tepotinib treatment was proven to be not cost-effective at the threshold of 40,000 USD/QALY in the Exponential model with MAIC (Figure 4).

Figure 4.

One-way sensitivity analysis and probabilistic sensitivity analysis. (A) One-way sensitivity analysis; (B) Cost-effectiveness acceptability curves; (C) Scatter plots. Abbreviations: ICE: incremental cost-effectiveness ratio; MAIC: matching-adjusted indirect comparison.

3.3. Scenario analysis

When the Weibull model with MAIC was applied, the estimated cost and utility of Tepotinib treatment were higher than those of Capmatinib treatment, respectively (103,789.94 USD vs. 51,933.79 USD; 2.16 QALYs vs 1.39 QALYs). The ICER of Capmatinib treatment vs. Tepotinib treatment was calculated at 67,307.46 USD/QALY, which suggested the Tepotinib treatment was not cost-effective compared to Capmatinib treatment as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations at a WTP threshold of 40,000 USD/QALY.

3.4. Subgroup analysis

When the time horizon of the model was 5 years, the estimated cost and utility of Tepotinib treatment were higher than those of Capmatinib treatment, respectively (88,108.83 USD vs. 50,421.84 USD; 1.77 QALYs vs 1.27 QALYs). The ICER of Capmatinib treatment vs. Tepotinib treatment was calculated at 75,877.37 USD/QALY. The result indicated that the Tepotinib was not cost-effective compared to the Capmatinib as second-line treatment for advanced NSCLC patients with a 5-year time horizon, which was consistent with that in a 10-year time horizon.

When all the SAEs occurred in both treatment arms, the estimated cost and utility of Tepotinib treatment were higher than those of Capmatinib treatment, respectively (95,392.69 USD vs. 53,892.13 USD; 2.02 QALYs vs 1.16 QALYs). The ICER of Capmatinib treatment vs. Tepotinib treatment was calculated at 48,142.52 USD/QALY. When none of the SAEs occurred in both treatment arms, the estimated cost and utility of Tepotinib treatment were higher than those of Capmatinib treatment, respectively (95,392.53 USD vs. 50,800.21 USD; 2.12 QALYs vs 1.41 QALYs). The ICER of Capmatinib treatment vs. Tepotinib treatment was calculated at 62,489.69 USD/QALY. The results suggested the Tepotinib treatment was not cost-effective compared to Capmatinib treatment as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations at a WTP threshold of 40,000 USD/QALY.

4. Discussion

We firstly applied PSM to evaluate the cost-effectiveness of Tepotinib compared to Capmatinib as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation in China. Based on the NCCN guideline (NSCLC, version 3, 2025), for the advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation, the second-line and subsequent therapy option was Capmatinib, Tepotinib, or Crizotinib [6]. As the cost-effectiveness evaluation of these treatment strategies was not clear in China, we conducted the cost-effectiveness analysis of Tepotinib compared to Capmatinib for advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation, providing a reference for clinical doctors, policymakers, and patients in clinical practice. Through searching several public databases, there was only one economic study evaluating the cost-effectiveness of Tepotinib vs Capmatinib for advanced or metastatic NSCLC patients with MET Exon 14 Skipping Mutation in the USA. The study showed Tepotinib was cost-effective compared with Capmatinib in front-line and line agnostic contexts, considering the range of willingness-to-pay thresholds recommended by the Institute for Clinical and Economic Review ($100,000-$150,000/QALY), while Tepotinib could be cost-effective in subsequent lines at higher willingness-to-pay levels [42]. Our study evaluated the cost-effectiveness of Tepotinib vs Capmatinib as second-line or subsequent treatment for NSCLC patients with MET Exon 14 Skipping Mutation in China, and the results were different from the previous study. The reasons for the different results were as follows: Our study calculated the cost based on recent drug expenses, which were different from those reported in the previous study in 2023. Besides, the other costs, utilities, discount rate, and the WTP threshold based on the Chinese environment in our study were also different from those in the previous study.

Our study used three times the GDP per capita as the WTP threshold, which was suggested in the China Guidelines for Pharmacoeconomic Evaluations (2020 Version) [38]. Besides, the results of INMB still showed that the Tepotinib was not cost-effective compared to Capmatinib as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations. As the selection of the cost-effectiveness threshold (CET) was important to the judgment of economy, it was necessary to study its value range. At present, except for the UK and Thailand, the majority of countries use 1–3 times the GDP per capita as the CET, which was recommended by the World Health Organization (WHO); however, there are still many problems with its scientificity and practicability. Opportunity cost refers to the maximum benefit that could be obtained in other aspects from the abandoned use of a resource when using it. The estimation of CET by the opportunity cost method could avoid the problem of traditional CET, assuming that the whole society was willing to invest the whole GDP in the field of health care, which would be helpful for health decision-makers to make scientific and reasonable decisions. Compared with the empirical method of 1–3 times the GDP per capita, the opportunity-cost-based threshold might lead to a more conservative CET. Therefore, the results obtained were at risk of being underestimated, and cost-effective interventions might be incorrectly excluded. Moreover, a lower CET might inhibit the enthusiasm of pharmaceutical enterprises to develop new products, resulting in the reduction of innovation ability. Based on the estimation of opportunity cost, China’s CET was 6347.1–9264.35 USD, which was lower than 1–3 times of the GDP per capita [43]. In our study, if we adopted the opportunity-cost-based threshold as the CET, Tepotinib was still not cost-effective compared to Capmatinib as the second-line treatment in advanced NSCLC patients with MET exon 14 skipping mutations.

According to our sensitive analysis, the results indicated that the factor of significant impact on the ICER of Tepotinib vs Capmatinib was the costs of Tepotinib and Capmatinib. This study performed a routine safety assessment to determine adverse event severity grades, adhering to the National Cancer Institute Common Terminology Criteria for Adverse Events (version 4.0) [44]. All SAEs were presumed to occur during the initial treatment cycle, and their costs were calculated by multiplying the specific adverse reaction management price by the occurrence rate [45]. In addition, to test the uncertainty of the occurrence of the SAEs, we assumed that all SAEs occurred or did not occur during the treatment period to determine their impact on the results in the subgroup analysis. The results suggested that Tepotinib was still not cost-effective compared to Capmatinib as the second-line treatment for the advanced NSCLC patients with or without SAEs. To explore the uncertainty of the time horizon, we conducted the cost-effectiveness analysis of Tepotinib vs Capmatinib in a 5-year time horizon, which indicated that as the time horizon shortened, Tepotinib was still not cost-effective compared to Capmatinib as second-line or subsequent treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations.

Our study had certain limitations. Firstly, since both GEOMETRY Mono-1 and VISION trials were single-arm studies without a common control arm, there would be some bias if we directly extracted the survival data from the two clinical trials, although the baseline characteristics of patients included in the two clinical trials were similar. As some factors were not reported in the two clinical trials at the same time, these factors might play an important role in the results of our study, such as patients’ smoking history, number of metastatic sites, lesion diameter, and lines of prior therapy (Table 5). Because there was no clinical trial comparing Tepotinib versus Capmatinib with the common control regimen, we could not conduct network meta-analysis, which was a commonly used indirect comparison method. As there was a study using MAIC to compare the survival data of Tepotinib versus Capmatinib, which was a pairwise indirect comparison method intended to provide a more accurate comparison of trial data by compensating for between-trial differences in patient characteristics, we applied the survival model with MAIC to conduct the cost-effectiveness analysis, hoping to decrease the bias of indirect comparisons. Secondly, the values of the utilities of health states were obtained from previously published studies, which might not accurately represent the health state of the patients in the Tepotinib arm and Capmatinib arm. Due to the fact that the clinical trials we referred to failed to calculate the QALYs of the patients, we obtained relevant quality-of-life scores by searching some relevant literature, which included metastatic NSCLC patients receiving the second-line treatment. Since the populations in these studies were similar to ours, we could refer to the quality-of-life scores of the patients in these studies to assess the economics of our study [34–37]. Thirdly, only the prices of SAEs were taken into consideration for this study, with the incidence rate surpassing 5% and the adverse reaction grading level exceeding or equal to 3. Although other adverse reactions might influence the results, the sensitivity analysis suggested that adverse reactions had a relatively minor impact on the robustness of the ICER.

Table 5.

Baseline demographics and clinical characteristics of the two clinical trials.

Characteristic	Tepotinib (n = 99)	Capmatinib (n = 100)
Median age, years	74.0	70.4
Sex
Male	54 (55%)	44 (44%)
Female	45 (45%)	56 (56%)
Race, n (%)
White	74 (75%)	73 (73%)
Asian	21 (21%)	24 (24%)
ECOG performance status score
0	22 (22%)	26 (26%)
1	77 (78%)	74 (74%)
Smoking history
Never smoked	/	59 (59%)
Former smoker	/	37 (37%)
Current smoker	/	4 (4%)
Histology
Adenocarcinoma	89 (90%)	78 (78%)
Squamous cell carcinoma	7 (7%)	10 (10%)
Patients with brain metastases	11 (11%)	17 (17%)
Number of metastatic sites
0	/	1 (1%)
1	/	6 (6%)
2	/	19 (19%)
3	/	21 (21%)
>3	/	53 (53%)
Lesion diameter at baseline by BIRC, mm	/	75.3 (46–88)
Lines of prior therapy for advanced/metastatic disease
0	43 (43%)	/
1	33 (33%)	/
≥2	23 (23%)	/

Abbreviations: ECOG: Eastern Cooperative Oncology Group; BIRC: blinded independent review committee.

5. Conclusions

Tepotinib was not cost-effective compared to Capmatinib as the second-line treatment for advanced or metastatic NSCLC patients with MET exon 14 skipping mutations in China.

Funding Statement

This manuscript was funded by the Scientific Research Project of Heilongjiang Provincial Health Commission (NO. 20251313010049) and Haiyan Foundation of Harbin Medical University Cancer Hospital (JJQN2023-09). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author contributions

All authors were involved in the conception and design. All authors were involved in the analysis and interpretation of the data. All authors were involved in drafting the paper and the revision of the manuscript. All authors agreed on the final version accepted for publication, and agreed to take responsibility and be accountable for the contents of the article and to share responsibility to resolve any questions raised about the accuracy or integrity of the published work.

Disclosure statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Ethics approval

Ethical approval was waived by the local Ethics Committee of Harbin Medical University Cancer Hospital because of the retrospective nature of the study, and all the procedures being performed were part of the routine care.

Data availability statement

All data generated or analyzed during this study are included in this article.