Cost-Effectiveness Analysis of 6 Tyrosine Kinase Inhibitors as First-Line Treatment for ALK-Positive NSCLC in China

Abstract

Background:

Lung cancer ranks first in both cancer incidence and mortality in China. The emergence of novel treatments for ALK-positive NSCLC led to an improvement in survival and quality of life for patients with advanced ALK mutation-positive non-small cell lung cancer (NSCLC). This study sought to assess the cost-effectiveness of 6 tyrosine kinase inhibitors (TKIs)—crizotinib, alectinib, ceritinib, brigatinib, ensartinib, and lorlatinib—as first-line treatments for ALK-positive NSCLC from the perspective of the Chinese health care system.

Methods:

A Markov model was developed to estimate the cost-effectiveness of these 6 TKIs. In this model, ALK-positive NSCLC patients were initially simulated to receive 1 of the 6 TKIs as first-line therapy, followed by different TKIs as subsequent treatment and salvage chemotherapy as last-line treatment. Survival data were sourced from the latest published clinical trials. Costs were derived from recent national health insurance negotiations and hospital information systems of selected health care facilities. Utilities for healthy states and adverse events were obtained from the literature. One-way and probabilistic sensitivity analysis as well as scenario analysis was conducted to assess the robustness of the results.

Results:

Compared to ensartinib, crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib demonstrated incremental quality-adjusted life years (QALYs) of −1.13, 0.39, −0.58, −0.09, and 0.35, respectively. The corresponding incremental costs were $10 677, $33 501, −$6426, $2672, and $24 358. This resulted in ICERs of –$9449/QALY, $85 900/QALY, $11 079/QALY, $29 689/QALY and $69 594/QALY, respectively.

Conclusion:

Crizotinib was considered to be absolutely dominated by ensartinib. Under a willingness-to-pay threshold of $38 223/QALY, ceritinib and brigatinib were cost-effective compared with ensartinib, while lorlatinib and alectinib were not cost-effective when compared with ensartinib. Overall, brigatinib emerged as the most cost-effective treatment among all the options considered.

Keywords

Cost-effectiveness non-small cell lung cancer anaplastic lymphoma kinase tyrosine kinase inhibitors China

Introduction

Lung cancer continues to be the leading cause of cancer-related deaths globally, comprising approximately 18% of total cancer fatalities, with more than 2 million new cases diagnosed annually. Besides, lung cancer is a prominent contributor to the burden of noncommunicable diseases worldwide. Of all lung cancers, 85% to 90% are classified as non-small cell lung cancer (NSCLC), and among these cases, anaplastic lymphoma kinase (ALK) gene rearrangement, also known as ALK fusion, is a significant mutation.^1,2 In China, the reported prevalence of ALK gene rearrangement (ALK-positive) among NSCLC patients is 5.3%. Notably, NSCLC is more prevalent among younger patients, with a median age of 52 years, and those with a history of smoking or light smoking (less than 10 packs/year).^3,4 Recent years have witnessed the development of various novel molecular targeted therapies for ALK-positive NSCLC, including tyrosine kinase inhibitors (TKIs).^5-7 One pivotal international multicenter, open-label, randomized, phase III clinical trial (PROFILE 1014, N = 343) demonstrated that ALK-positive NSCLC patients treated with ALK inhibitors experienced significantly improved median progression-free survival (PFS, 10.9 months vs 7.0 months, P < .01) and overall response rate (ORR, 74% vs 45%, P < .01) compared with those receiving alkylating-based dual-combination chemotherapy.⁸ Crizotinib has shown efficacy in NSCLC cases with classical EML4-ALK fusions, multiple noncanonical ALK fusions, or ALK complex fusions. Owing to its favorable survival outcomes, crizotinib gained approval from the US Food and Drug Administration (FDA) in 2011 and served as the primary first-line therapy for ALK-positive advanced NSCLC patients for several years. In China, crizotinib has been established as the standard of care.⁹ Despite the emergence of more potent ALK inhibitors (such as alectinib, ceritinib, and ensartinib) that have demonstrated superior clinical outcomes compared with crizotinib as first-line therapies, some jurisdictions still recommend crizotinib as the standard of care for ALK-positive patients based on economic evaluations.^10-12 Nevertheless, crizotinib limited penetration of the blood-brain barrier accounts for suboptimal exposure in the cerebrospinal fluid and the inevitable development of acquired resistance.

Second-generation TKIs (alectinib, ceritinib, brigatinib, and ensartinib) and third-generation TKIs (lorlatinib) have yielded substantial survival improvements in ALK-positive NSCLC patients, along with enhanced pharmacological activity and blood-brain barrier penetration.^13-17 Ensartinib, developed in China, received approval as a second-line targeted therapy following progression on crizotinib and was recommended as a first-line targeted therapy for ALK-positive patients in the 2023 Chinese Society of Clinical Oncology (CSCO) guidelines. Lorlatinib, a third-generation ALK inhibitor, obtained FDA approval in 2019 for use in adult ALK-positive metastatic NSCLC patients who had progressed on crizotinib and at least one other ALK inhibitor. Results from an international randomized phase III trial (CROWN study) showed that previously untreated advanced ALK-positive NSCLC patients receiving lorlatinib experienced significantly longer PFS and a higher intracranial response rate compared with those receiving crizotinib. In addition, patients treated with lorlatinib exhibited a significantly prolonged time to central nervous system (CNS) progression¹⁶. In March 2021, the FDA expanded the drug’s indication, permitting first-line use of lorlatinib for adult ALK-positive NSCLC patients who had not previously received ALK inhibitors. As a result, lorlatinib now serves not only as a subsequent treatment following resistance to first- and second-generation inhibitors but has also become a first-line option, offering more treatment choices for newly diagnosed ALK-positive patients.

Consequently, second- and third-generation TKIs are now recommended as the first-line treatment for ALK-positive NSCLC according to NCCN and CSCO guidelines. Brigatinib and lorlatinib were approved to enter Chinese market. So far, the first-line treatment drugs recommended by the CSCO guidelines for treating ALK-positive NSCLC, including crizotinib, alectinib, ceritinib, brigatinib, lorlatinib, and ensartinib, have all included in the 2023 National Medical Insurance Drug List through national negotiations. With the introduction of new drugs and changes in prices, it is increasingly imperative to determine the most cost-effective among these TKIs. This study undertakes a cost-effectiveness analysis of crizotinib, alectinib, ceritinib, brigatinib, ensartinib, and lorlatinib as first-line treatments for ALK-positive advanced NSCLC from the perspective of the Chinese health care system, aiming to provide valuable insights for drug price negotiations and clinical decision-making in China.

Methods

Perspective

Our analysis was conducted from the perspective of the Chinese health care system, which involves assessing the utilization of health care resources and the benefits of interventions within the context of the national health care system. China’s health care system comprises various levels, with primary reliance on basic medical insurance and supplementary services like commercial health insurance, charitable donations, medical mutual aid activities, and medical aid as a backup.¹⁸

Model structure

To account for patient heterogeneity, a Markov model was employed to evaluate the cost-effectiveness of 6 TKIs (crizotinib, alectinib, ceritinib, brigatinib, ensartinib, and lorlatinib) as first-line treatments for ALK-positive NSCLC patients using TreeAge Pro software Version 2022. The Markov model operated in monthly cycles, with a time horizon of 10 years. We selected the 10-year timeframe based on the follow-up duration of current clinical studies since long-term survival data for ALK-positive advanced NSCLC patients is currently lacking. The Markov model begins with first-line treatment, where ALK-positive NSCLC patients receive 1 of the 6 TKIs. Following disease progression, patients have the option of receiving appropriate ALK inhibitors as subsequent treatments (second-line). Finally, patients who experience multiple rounds of progression undergo salvage chemotherapy as the last-line treatment. The selection of ALK+ inhibitors as subsequent treatment was based on CSCO guidelines and evidences from clinical trials. In this study, lorlatinib was employed as subsequent treatment following progression in the crizotinib, alectinib, ceritinib, brigatinib, and ensartinib cohort, while ensartinib was used as subsequent treatment following progression in the lorlatinib cohort. Based on clinical evidence, there is a certain probability of brain metastasis following first-line AKIs treatment. Therefore, in our model, we also considered the costs related to brain metastasis and the reduction in quality of life. A simplified diagram of the Markov model was depicted in Figure 1, Fig.S1 and Fig.S2.

Figure 1.

Model structure for Markov model. (1) Patients enter the Markov model in the progression-free state and they receive first-line TKIs (ensartinib, crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib). Patients in the progression-free state may remain progression-free, their cancer may progress with or without CNS involvement, or they may die. (2) Patients with non-CNS progressed disease may remain in their current state, experience CNS progression, or die. (3) CNS-progressed patients can remain alive with CNS-progressed disease or die. (4) Death is an absorbing state.

Clinical data inputs

We obtained Kaplan-Meier (KM) survival curves for modeling overall survival (OS) and PFS from various sources, including the PROFILE1014,⁸ J-ALEX,¹³ ASCEND-4¹⁴,ALTA-1L,¹⁵ CROWN,¹⁶ eXalt3,¹⁹ NCT01970865,¹⁷ NCT03215693,²⁰ and NCT00789373,²¹ and extracted data points using GetData Graph Digitizer version 2.26. Subsequently, we estimated pseudo-individual data through maximum likelihood functions based on the extracted values and the number of surviving patients at each time interval in R 4.0.3 using an algorithm developed by Guyot et al.²² Finally, we applied parametric survival models, including Weibull, Log-logistic, Log-normal, Gompertz, Exponential, Gamma, and Generalized Gamma distributions, to fit the pseudo-individual data.²³ The result shows that AIC were within 5 points across models, suggesting there is not a difference in the goodness of fit to the observed data. The selection of the best-fitting distribution was based on statistical information criteria, visual inspection of the curve, and discussion with clinical experts. The Weibull curves provide the most conservative survival estimate in treatment arms. Finally, Weibull curves are selected to model in the 6 ALKs arms (refer to Table S1–1, TableS1–2, Table S1–3 and Fig S3-S4). We also incorporated the 2022 China life table into the model-adjusted long-term background mortality rates.

Cost and utility

Since our study adopts the perspective of the Chinese health care system, we considered only the following direct costs: drug acquisition, routine follow-up expenses, supporting care costs, salvage chemotherapy expenses, hospice costs, treatment-related adverse event (AE) costs, and a one-time end-of-life care cost, regardless of whether the death was related to NSCLC (refer to Table 1). Prices for crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib were obtained from the Zhejiang Provincial Center for Drug & Medical Device Procurement (ZJCDDP). Costs for salvage chemotherapy, routine follow-up, supportive care, support care for CNS metastases per cycle, support care for no CNS metastases per cycle, end-of-life care, and treatment-related AEs were sourced from published studies.^25,27,28

Table 1.

Input parameters.

Model input	Baseline value	Range	Distribution	Sources/notes
Utility
Progression free	0.856	0.728-0.984	Beta	Shen et al²⁴
Non-CNS progressed	0.768	0.653-0.883	Beta	Shen et al²⁴
CNS progressed	0.470	0.399-0.541	Beta	Liu et al²⁵
Disutility for CNS metastases
Crizotinib	0.156	0.125-0.187	Beta	Sivignon et al¹⁰
Alectinib	0.044	0.035-0.053	Beta	Sivignon et al¹⁰
Ceritinib	0.071	0.057-0.085	Beta	Sivignon et al¹⁰
Brigatinib	0.047	0.038-0.056	Beta	Sivignon et al¹⁰
Lorlatinib	0.013	0.010-0.016	Beta	Nafees et al²⁶
Ensartinib	0.020	0.016-0.024	Beta	Nafees et al²⁶
Disutility for AEs
Crizotinib	0.021	0.017-0.025	Beta	Public literature^10,26
Alectinib	0.011	0.009-0.013	Beta	Public literature^10,26
Ceritinib	0.059	0.047-0.071	Beta	Public literature^10,26
Brigatinib	0.029	0.023-0.035	Beta	Public literature^10,26
Lorlatinib	0.034	0.027-0.041	Beta	Public literature^10,26
Ensartinib	0.012	0.010-0.014	Beta	Public literature^10,26
Probability of CNS metastasis
Crizotinib	0.332	0.267-0.398	Beta	Nakagawa et al¹³
Alectinib	0.094	0.075-0.113	Beta	Nakagawa et al¹³
Ceritinib	0.150	0.120-0.180	Beta	Soria et al¹⁴
Brigatinib	0.100	0.080-0.120	Beta	Camidge et al¹⁵
Lorlatinib	0.028	0.022-0.034	Beta	Shaw et al⁷
Ensartinib	0.042	0.034-0.050	Beta	Horn et al¹⁹
Cost,$
Crizotinib per cycle	1531.96	1378.76-1516.64	Gamma	ZJCDDP
Alectinib per cycle	2258.30	2032.47-2235.72	Gamma	ZJCDDP
Ceritinib per cycle	1448.93	1304.04-1434.44	Gamma	ZJCDDP
Brigatinib per cycle	1513,21	1361.89-1664.53	Gamma	ZJCDDP
Lorlatinib per cycle	2351.51	2116.36-2327.99	Gamma	ZJCDDP
Ensartinib per cycle	1487.01	1338.31-1472.14	Gamma	ZJCDDP
Chemotherapy per cycle	1409.10	1268.19-1395.01	Gamma	ZJCDDP
Follow-up per day	85.71	68.57-102.85	Gamma	Lu et al²⁷
PFS Supportive care per cycle	106.76	85.41-128.11	Gamma	Lu et al²⁷
Palliative care in end of life	1892.7	1514.16-2271.24	Gamma	Lu et al²⁷
Support care CNS metastases per cycle	3538.00	2830.40-4245.60	Gamma	Liu et al²⁵
Support care no CNS metastases per cycle	824.70	659.76-989.64	Gamma	Liu et al²⁵
AEs cost for crizotinib	1337.53	1070.02-1605.04	Gamma	Wong et al²⁸
AEs cost for alectinib	969.92	775.94-1163.90	Gamma	Wong et al²⁸
AEs cost for ceritinib	2503.18	2002.54-3003.82	Gamma	Wong et al²⁸
AEs cost for brigatinib	1452.63	1162.10-1743.16	Gamma	Wong et al²⁸
AEs cost for lorlatinib	1999.42	1599.54-2399.30	Gamma	Wong et al²⁸
AEs cost for ensartinib	313.67	250.94-376.40	Gamma	Wong et al²⁸
AEs cost for chemotherapy	1409.10	1127.28-1690.92	Gamma	Wong et al²⁸

Abbreviations: AEs, adverse events; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CNS, central nervous system; PFS, progression-free survival; ZJCDDP, Zhejiang Provincial Center for Drug & Medical Device Procurement.

Health utility value refers to the weight assigned to different health states relative to complete health, serving as an indicator to assess the satisfaction level of a particular health state. The health utility values for stable disease and progressive disease in this study were derived from previously published research (refer to Table 1).²⁴ Disutility due to CNS metastasis and AEs were also considered in these states.^10,25,26

Main outcomes

The health care cost and outcomes assessed in this study included cost, disease-free life years, total life years, and quality-adjusted life years (QALYs). All costs and QALYs were discounted at a rate of 5% per year, following the China Guidelines for Pharmacoeconomic Evaluations 2020 (Guidelines). Costs were presented in 2022 US dollars, applying an exchange rate of 6.7208. According to the Guidelines, the willingness-to-pay (WTP) threshold per QALY was set at 3 times the national per capita gross domestic product (GDP) in the incremental analysis. In 2022, China’s per capita GDP stood at $12741 (sourced from the National Bureau of Statistics website), thus setting the WTP threshold at $38223. The cost-effectiveness analysis was performed using TreeAge 2022 (TreeAge Software, Inc. Williamstown, MA, USA).

Sensitivity analyses

To assess the uncertainty of parameters and assumptions, we conducted both univariate sensitivity analysis and probabilistic sensitivity analysis to evaluate the robustness of the base case results. In univariate sensitivity analysis, parameters were independently varied within a plausible range based on published data or 95% confidence intervals (CI). In cases where this was not applicable, values were varied by ±10% to ±20% of the corresponding base case value. Probabilistic sensitivity analysis was executed using second-order Monte Carlo simulation with 10 000 iterations to account for uncertainty in model parameters. We employed gamma distributions for costs, beta distributions for utilities, and time-dependent transition probabilities. In addition, various scenario analyses were conducted to explore methodological and structural uncertainty, including testing alternative discount rates, Gamma distributions, exponential distributions, 20-year time horizon, and starting age.

Results

Base case analysis

The base case analysis spanning a period of 10 years showed that 88.89%, 99.25%, 80.66%, 95.84%, 84.78%, and 77.61% of the patients in the ensartinib, crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib group had died, 2.41%, 0.47%, 2.88%, 1.44%, 2.41%, and 2.24% of the patients were in the salvage chemotherapy stage, 2.82%, 0.28%, 3.62%, 1.42%, 2.94%, and 1.26% of the patients were in the second-line therapy stage and 5.88%, 0%, 12.84%, 1.30%, 9.87%, and 18.89% of the patients were in the PFS stage, with a cumulative cost and cumulative effect of $153 642, $164 319, $187 143, $147 216, $156 314, and $178 000 and 3.58, 2.45, 3.97, 3.00, 3.67, and 3.93 QALYs, respectively (Fig S4). By the 10th year, crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib provided incremental gains of −1.13, 0.39, −0.58, 0.09, and 0.35 QALYs in patients with ALK-positive NSCLC with incremental cost reductions of $10 677, $33 501, −$6426, $2672, and $24 357 compared with the ensartinib. This resulted in ICERs of $9449/QALY, $85 900/QALY, $11 079/QALY, $29 689/QALY, and $69 594/QALY, respectively (Table 2 and Figure 2).

Table 2.

Summary base case results.

Strategy	Cost ($)	QALYs	incrCost ($)	incrQALYs	ICER ($/QALY)	NMB
Ensartinib	153 642	3.58	/	/	/	–16 896
Crizotinib	164 319	2.45	10 677	–1.13	Absolute Dominated	–70 506
Alectinib	187 143	3.97	33 501	0.39	85 ,900	–35 386
Ceritinib	147 216	3.00	–6426	–0.58	11 079	–32 401
Brigatinib	156 314	3.67	2672	0.09	29 689	–15 997
Lorlatinib	178 000	3.93	24 358	0.35	69 594	–27 883

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

Figure 2.

Cost-effectiveness analyses.

The result of cost-effective analysis showed that alectinib as first-line treatment generated the highest QALYs, while ceritinib incurred the lowest cost. Compared to ensartinib, crizotinib experienced higher costs and lower QALYs, and was absolute dominated by ensartinib. The alectinib and lorlatinib were not cost-effective compared with ensartinib under WTP threshold of $38 223; the brigatinib and ceritinib were cost-effective compared with ensartinib. While brigatinib was not associated with the greatest QALY benefits among all the AKIs considered in this study, it emerged as the most cost-effective first-line treatment.

Sensitivity analyses

The results of the one-way sensitivity analysis presented in Figure 3 revealed that the primary drivers of the model’s outcome include drug prices of the 6 TKIs, the utility of progression free disease (PFD) and progressive disease (PD), the probability of central system metastasis, discount, and the cost of support care CNS metastases per cycle. Acceptance curves in Figure 4 demonstrate that as the WTP threshold increased to $50 000, the probabilities of cost-effectiveness for crizotinib, alectinib, ceritinib, brigatinib, lorlatinib, and ensartinib were 5.60%, 11.4%, 3.10%, 31.40%, 13.5%, and 35.00%, respectively. The cost-effectiveness scatter plot for the 6 TKIs in the treatment of ALK-positive NSCLC patients is presented in Figure 5 compared to ensartinib, crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib exhibited probabilities of being considered cost-effective at the WTP threshold of $38 223/QALY in 14.6%, 28.6%,13.1%,59.5%, and 39.4% of all simulations, respectively. Thus, the model results were relatively robust according to the sensitivity analysis findings. The scenario analysis (Table 3) revealed that the utilization of exponential extrapolation for the 6 TKIs had the most significant impact on the ICER, with ensartinib emerging as the most cost-effective treatment.

Figure 3.

One-way sensitivity analysis (Tornado diagrams of ICER, the Pink bars represents low parameter value and the purple bars represents low parameter value). (A) crizotinib vs ensartinib; (B) alectinib vs ensartinib; (C) ceritinib vs ensartinib; (D) brigatinib vs ensartinib; and (E) lorlatinib vs ensartinib.

Figure 4.

Cost-effectiveness acceptance curves.

Figure 5.

Scatter plot of ICER (each dot demonstrates one iteration in the Monte Carlo simulation. The green circle represents the 95% confidence interval. Red dots show iterations that are not cost-effective at the WTP threshold. Green dots show iterations that are cost-effective at the WTP threshold). (A) crizotinib vs ensartinib; (B) alectinib vs ensartinib; (C) ceritinib vs ensartinib; (D) brigatinib vs ensartinib; and (E) lorlatinib vs ensartinib.

Table 3.

Scenario analysis results.

Scenario	ICER (ensartinib as reference); $
Scenario	Crizotinib	Alectinib	Ceritinib	Brigatinib	Lorlatinib
Base case	–9449	85 900	11 079	29 689	69 594
Discount rate—0%	–13 241	90 659	9062	32 528	76 966
Discount rate—8%	–13 236	90 652	9064	32 523	76 956
Gamma extrapolation	–8925	83 392	11 014	28 187	69 037
Exponential extrapolation	–18 198	79 837	6816	50 462	106 521
20-year time horizon	–3395	73 858	16 065	29 202	55 189
The starting age is 35	–7061	85 326	13 437	32 479	69 602

Discussion

It is acknowledged that the additional value of a novel treatment should be priced on the basis of the benefit, the preferably gain in survival and the delay in progression are acceptable as it is a vital aim for patients.²⁹ In the last few years, new treatments especially with the advent of new targeted therapies in standard of care were approved based on improvements of PFS, without a corresponding increase in OS. In addition, the mandatory task for current health systems are the need to better stratify and correctly prescribe the best medication to the right patients to optimize outcome, reduce AEs and costs, and rationalize resources.³⁰

This study represents the first attempt to assess the cost-effectiveness profile of 6 TKIs (crizotinib, alectinib, ceritinib, brigatinib, ensartinib, and lorlatinib) recommended by the CSCO guidelines for the treatment of ALK-positive NSCLC patients from the perspective of the Chinese health care system. In addition, we incorporated appropriate TKIs as subsequent therapies after progression to align with the latest clinical evidence and practices. Previous models conducted for the cost-effectiveness of TKIs did not consider the use of TKIs in subsequent treatments.¹² Our study suggests that brigatinib emerged as the most suitable first-line treatment for ALK-positive NSCLC patients, which was consistent with previous study.³¹ Followed by enshatinib and loratinib was thirdly.

However, Naik J and colleagues exhibited that lorlatinib can be considered a cost-effective treatment option for first-line patients with ALK-positive NSCLC in Sweden when compared with crizotinib, alectinib, and brigatinib.³² Moreover, compared with first- and second-generation TKIs for previously untreated patients with ALK-positive advanced NSCLC, lorlatinib provides substantial clinical benefit and appears to be a cost-effective treatment option in Greece.³³ It is maybe due to the difference of the medical price system between TKIs in Sweden, Greece, and China, respectively. In China, brigatinib is relatively low cost per cycle in China. Recent studies have explored the cost-effectiveness of ceritinib versus crizotinib for previously untreated ALK-positive NSCLC patients in the United States based on indirect comparative efficacy.³⁴ In this context, the ICER for ceritinib versus crizotinib was $66 064, suggesting that ceritinib represents a more cost-effective first-line treatment option. A similar assessment was conducted from the perspective of Hong Kong’s health care system, with the ICER for ceritinib versus crizotinib estimated at $13 343, reinforcing the view that ceritinib offers enhanced cost-effectiveness.³⁵ Notably, a 2019 Chinese study demonstrated that compared with crizotinib, ceritinib, and alectinib produced ICERs of $84 728 and $339 114 for ALK-positive NSCLC patients. At that time, crizotinib was deemed the optimal first-line targeted drug from a cost-effectiveness analysis standpoint.²⁵ Our study also confirms the cost and QALY benefits of ceritinib compared with crizotinib.

Our results differ from those of a previous Chinese study.¹² First, with the primary reason attributed to the implementation of medical insurance drug negotiations, which have resulted in substantial price discounts for the negotiated drugs. The prices of the latest negotiated drugs, as of December 2022, have seen average reductions of 60.1%, significantly increasing the accessibility of these medications. As of March 1, 2023, the latest medical insurance list has come into effect, crizotinib, alectinib, ceritinib, brigatinib, lorlatinib, and ensartinib have all been included in this medical insurance list. Except for alectinib, the prices of the other 5 drugs have been reduced to varying extend. Among them, lorlatinib and brigatinib have been included for the first time since they entered the Chinese market last year, and they have experienced significant price reductions compared with original price. Given these price reductions, our study holds significance in conducting economic assessments to confirm the value for money of these TKIs, thus providing valuable insights for innovative drug price negotiations for medical insurance access and informing clinical decision-making. Second, this study fully considered the probability of brain metastasis of each TKIs and calculated the corresponding additional cost and utility respectively.

Nevertheless, there were several limitations to this study. First, transitions between health states for the 5 TKIs cohorts were informed by parametric models fitted to PFS and OS data from clinical trials conducted abroad. It should be borne in mind that the subjects of these clinical trials did not exclusively comprise the Asian population, and ethnic differences and uncertainties in the results may exist. Second, the utility values for health states were also derived from foreign studies rather than domestic ones, potentially introducing biases into the model results. Finally, this study did not consider grade 1 or 2 AEs, which might lead to an overestimation of the economic profile of individual targeted agents. Nonetheless, the one-way sensitivity analysis results indicated that the costs associated with AEs would only have a mild impact.

Conclusion

In summary, compared with crizotinib, alectinib, and lorlatinib, ensartinib was considered to be cost-effective from the perspective of the Chinese health care system. Compared to ensartinib, ceritinib, and brigatinib were cost-effective. Among all 6 TKIs, brigatinib was considered to be the most cost-effective treatment. These findings hold potential significance for physicians, patients, and policymakers in China.

Supplemental Material

sj-docx-1-onc-10.1177_11795549241257234 – Supplemental material for Cost-Effectiveness Analysis of 6 Tyrosine Kinase Inhibitors as First-Line Treatment for ALK-Positive NSCLC in China

Supplemental material, sj-docx-1-onc-10.1177_11795549241257234 for Cost-Effectiveness Analysis of 6 Tyrosine Kinase Inhibitors as First-Line Treatment for ALK-Positive NSCLC in China by Meiling Zhang, Bei Zheng, Wenjuan Yang, Hong Jiang, Xueshan Sun, Zixuan Zhao, Gonghua Li and Hengjin Dong in Clinical Medicine Insights: Oncology

Footnotes

Acknowledgements

The authors thank Home for Researchers editorial team () for language editing service.

Author Contributions

MZ contributed to the data collection, founding acquisition, investigation, data analysis and interpretation, methodology, software, writing—original draft, writing—review and editing. BZ contributed to the data collection, software and investigation. WY and HJ contributed to the data collection and investigation. XS and ZZ contributed to the conceptualization and methodology. GL contributed to the conceptualization and investigation. HD contributed to the conceptualization, investigation, supervision, methodology, writing—review and editing. All listed authors approved the final version of the manuscript.

Funding:

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Basic Public Welfare Research Program of Zhejiang Province, grant no. LGF20G030004 and Zhejiang Pharmaceutical Association (grant no. 2022ZYJ08).

Declaration of conflicting interests:

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

Availability of Data and Materials

The original data presented in this study are available in the Manuscript and Supplementary Material. Further inquiries can be directed to the corresponding author upon reasonable request.

Consent for Publication

Not applicable.

Ethics Approval and Consent to Participate

Not applicable.

ORCID iD

Hengjin Dong

Supplemental Material

Supplemental material for this article is available online.

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