Cost-effectiveness of Metronomic chemotherapy vs Weekly Intravenous Paclitaxel in patients with ER+/HER2-Metastatic Breast Cancer

Cost-effectiveness of Metronomic chemotherapy vs Weekly Intravenous Paclitaxel in patients with ER+/HER2-Metastatic Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cost-effectiveness of Metronomic chemotherapy vs Weekly Intravenous Paclitaxel in patients with ER+/HER2-Metastatic Breast Cancer Ning Ren, Qiaoping Xu, Lanqi Ren, Yibei Yang, Junjie Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3860294/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective ：To compare the cost-effectiveness of Metronomic Oral Vinorelbine plus Cyclophosphamide and Capecitabine(VEX) and Weekly Intravenous Paclitaxel (P) in patients with Estrogen Receptor–Positive, ERBB2-Negative Metastatic Breast Cancer (MBC). Methods ：The Markov model was established to simulate the patients receiving metronomic chemotherapy (VEX regimen) and Weekly Intravenous Paclitaxel. The results of clinical trials and other published literature were comprehensively used to evaluate the cost-effectiveness ratio of the two chemotherapy regimens, and sensitivity analyses were conducted. Results ：The QALYs of VEX and P regimen were 1.85 and 1.45, respectively, and the ICERs were $40 333.69/QALY and $4 152.09/QALY, respectively. In China, the total cost of VEX regimen is $74 617.32, while the total cost of P regimen is $6 020.53. The cost of P regimen is much lower than that of the VEX regimen. In addition, the VEX is more effective than the P, with higher TTF and PFS, and higher disease control rates. Sensitivity analysis shows that P regimen has a more cost-effective advantage in China. The analysis of incremental cost-effectiveness shows that with VEX as the reference group, P regimen is the preferred option. Conclusions ：Compared with VEX, P regimen is more cost-effective as a first-line treatment for ER+/HER2- metastatic breast cancer from the perspective of Chinese health service system. ER+/HER2- metastatic breast cancer metronomic chemotherapy Pharmaceutical economics Markov model Cost-effectiveness analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In most countries, breast cancer remains the most diagnosable cancer and the leading cause of cancer deaths in women worldwide. It was estimated that there were approximately 2.3 million patients with newly diagnosed breast cancer, accounting for 11.7% of all new cancer cases, and more than 680 000 deaths worldwide in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer(Sung et al. 2021 ). The incidence of breast cancer in Asia has escalated over recent decades, especially among younger women(Yeo et al. 2019 ). The incidence rate in China also shows an increasing trend year by year. Around 70% of breast cancers express the estrogen receptor alpha (ERα) and depend on estrogen for growth and disease progression. Although most ER-positive/HER2-negative (ER+/HER2−) breast cancers are associated with good prognosis, around one third of patients progress and develop late recurrences, sometimes even decades after initial diagnosis and treatment(Rosswag et al. 2021 ). Endocrine therapy for estrogen receptor/human epidermal growth factor receptor 2 (ER+/HER2–) tumors has greatly contributed to reduce early breast cancer recurrence. However, resistance towards endocrine therapies occurs often, and is found in nearly all hormone receptor positive breast cancer patients with metastatic disease. Metastatic breast cancer (MBC) treatment remains a significant clinical issue(Seki et al. 2019 ). In clinical practice, most patients with ER+/HER2- metastatic breast cancer will receive chemotherapy. In order to improve the efficacy of chemotherapy, the maximum tolerable dose of cytotoxic drugs is often used as the standard dose for treatment. However, due to the high toxicity, patients cannot tolerate it for a long time, which limits its long-term use in clinical practice. Metronomic chemotherapy is an emerging treatment mode in recent years, which uses low-dose chemotherapy drugs equivalent to conventional doses of 1/10 − 1/3, continuously or high-frequency (1–3 times a week) administration, targeting activated endothelial cells within the tumor as the treatment target. This approach significantly reduces toxicities and the need for growth factor support to accelerate recovery from myelosuppression(Montagna et al. 2018 ). Compared to traditional chemotherapy, metronomic chemotherapy has its unique mechanism of action, which is to shorten the treatment interval, on the one hand, reduce the repair of tumor cells during the treatment interval, and achieve direct tumor inhibition; On the other hand, it exerts anti-tumor effects through anti-tumor angiogenesis and immune regulation. At the same time, the dosage of metronomic chemotherapy is relatively low, which can maintain a relatively low but effective blood drug concentration for a long time, in order to prolong disease control time and reduce toxic side effects. Therefore, it has the advantages of low adverse reactions, low drug resistance, low treatment costs, and easy long-term administration. Based on the characteristics of metronomic chemotherapy, oral medication is generally preferred. The metronomic combination of capecitabine, vinorelbine, and cyclophosphamide (VEX regimen) was recently investigated in a phase II trial of patients with endocrine-responsive MBC. METEORA-II trial is a multicenter, open label, randomized controlled phase II clinical trial to evaluate the efficacy and safety of vinorelbine + Cyclophosphamide + Capecitabine (VEX regimen) in ER+/HER2 metastatic breast cancer patients(Munzone et al. 2023 ). This study included a total of 140 patients who were randomly divided into the VEX group and the P group. 133 patients were included in the efficacy analysis set, including 70 patients in the VEX group and 63 patients in the P group. At the final analysis of the study, the median follow-up time was 29 months, with 128 TTF events and 61 deaths among 133 patients. Compared with the P group, the VEX group showed significant improvements in TTF and PFS: median TTF was 8.3 months and 5.7 months, median PFS was 11.1 months and 6.9 months, and the 12 months PFS rates were 43.5% and 21.9%, respectively. There was no significant difference in OS between the two groups. The METEORA-II trial showed that VEX metronomic chemotherapy significantly increased TTF compared to single paclitaxel P, and for ER+/HER2-MBC patients who plan chemotherapy, the metronomic chemotherapy VEX regimen can be considered as a first-line chemotherapy regimen. Therefore, based on the METEORA-II trial, we evaluated the cost-effectiveness of the full oral rhythm chemotherapy regimen vinorelbine + Cyclophosphamide + Capecitabine (VEX regimen) and weekly intravenous paclitaxel (P). Methods Statement According to the ministry of health "measures on ethical review of biomedical research involving human beings (trial 2007)"、WMA《Declaration of Helsinki》 and the ethical principles of CIOMS《the international moral guide to human biological research》,Subject to review by the ethics committee, Agree to carry out this study according to the research regimen under review. Design and structure of the model A decision- analytic Markov model was developed using TreeAge Pro Suit 2011 to analyze the cost-effectiveness of VEX metronomic chemotherapy or weekly intravenous paclitaxel for ER+/HER2- metastatic breast cancer patients. After the specified treatment, the patient will be in one of the following four health states: remission, stability, relapse and death, as shown in the model in Fig. 1. Each circle in Fig. 1 represents a state, and the arrows represent transitions between states. A patient can move from a healthy state to a disease state and from a disease state to a healthy state. The patient may die in either a healthy state or a state of illness, that is, transfer to a state of death. We set the utility weight and cost of each cycle for each health state, and estimate the movement between each health state according to the conversion probability calculated in clinical trials. The cycle length is one year, and it is adjusted to half cycle in each health state process(Huang et al. 2020; Rui et al. 2020 ). We calculated the change in the proportion of patients present in each state over time, as well as the cumulative cost and quality-adjusted life years (QALYs) of patients in a certain state of health for each period of time. A quality-adjusted life year (QALY) is estimated as the time spent in each health state multiplied by the utility associated with the health state(Gogate et al. 2019 ; Austin et al. 2020 ). The discount rate is an important parameter in pharmacoeconomic evaluation and is a parameter of the time value of the reaction cost used in the calculation of cost equivalence. According to the Chinese Pharmacoeconomic Evaluation Guidelines (2011 edition)(《中国药物经济学评价指南》课题组 et al. 2011),the internationally recommended 5% discount rate was selected in our study to discount the cost and quality years. In the baseline analysis, the cost is calculated using a 5% discount rate, and in the sensitivity analysis, the incremental cost utility ratio is calculated using a 0% -5% discount rate to examine the stability of the results. The incremental cost-effectiveness ratio (ICER) is defined as a ratio of incremental costs to incremental benefits, and we represent the results in ICERs based on the cost of each QALY obtained(Ward et al. 2020 ). In order to conduct probabilistic sensitivity analysis (PSA), we used second-order Monte Carlo analysis (1000 simulations) to simulate the model and constructed confidence intervals using bootstrap replications (1000 replications). Patients and treatment plans The METEORA-II trial enrolled 140 female patients with ER+/HER2- metastatic breast cancer whose age was ≥ 18 years from 15 medical centers in Italy between September 13, 2017, and January 14, 2021. Patients could receive 1-line chemotherapy and/or 2-line endocrine therapy in the past, including CDK4/6 inhibitors. Patients were randomly divided into two groups in a 1:1 ratio: the VEX group and the paclitaxel group. VEX group: Take orally 40 mg vinorelbine, 50 mg Cyclophosphamide and 500 mg Capecitabine three times a day on the first, third and fifth days of each week until the disease progresses or is intolerable. Paclitaxel group: Intravenous injection of paclitaxel (90 mg/m 2 ) every 4 weeks on days 1, 8, and 15 until disease progression or intolerance(Montagna et al. 2018 ). Model inputs for transition probabilities The transition probability is the probability that an event will occur in a specific period of time, and the transition probability in Markov's model is the probability of being in a different state of health during the cycle. In this study, the DEALE method was used to calculate the transition probability by using the data of the phase II clinical trial(Munzone et al. 2023 ), and the transition probability calculation process is shown in Table 1 . In addition, we converted all hazard ratios and survival rates into transition probabilities for one-year time periods. The log-logistic model is suitable for describing PFS and OS, and we calibrate the transition probability from the actual overall lifetime (OS) data and median time to progression and feed it into the Markov model(Diaby et al. 2016 ). Use the chi-square test to compare the differences between the actual OS data and the model data derived from our Markov state. Model inputs for effectiveness Health utility index refers to the weight of different health states relative to complete health in all health states, which is an index for evaluating the satisfaction of a certain health status, and is a comprehensive index reflecting the health status of individuals. Each state of health utility index is between 0–1, with 0 representing death and 1 being fully healthy. The health utility values for this study were derived from the health utility index used in previously published analyses(Ward et al. 2007 ; Paracha et al. 2016 ), as shown in Table 1 . These utilities were chosen because their settings are similar. Table 1 Key parameters for the model of adjuvant treatment of regional breast cancer with VEX regimen and P regimen Variable Formula Best estimate SA range Distribution Probability --VEX RR 0.6237 OS PFS 29.5 11.1 DOR 8.3 Stable → Stable (VEXss) 1-VEXsp-VEXsr 0.5698 0.5128–0.6268 Beta Stable → Remission (VEXsr) 1-exp(-RR/3) 0.1878 0.1690–0.2066 Beta Stable → Relapse (VEXsp) VEXrp*4 0.2424 0.2182–0.2666 Beta Remission → Remission (VEXrr) 1-VEXrp 0.9394 0.8455-1.0000 Beta Remission → Relapse (VEXrp) 1-exp(-0.75*In(2)/(DOR)) 0.0606 0.0545–0.0667 Beta Relapse → Relapse (VEXpp) 1-VEXpd 0.9721 0.8749-1.0000 Beta Relapse → Death (VEXpd) 1-exp(-0.75*In(2)/(OS-PFS)) 0.0279 0.0251–0.0307 Beta --P RR 0.7952 OS PFS 33.7 6.9 DOR 5.7 Stable → Stable (Pss) 1-Psp-Psr 0.4183 0.3765–0.4601 Beta Stable → Remission (Psr) 1-exp(-RR/3) 0.2329 0.2096–0.2562 Beta Stable → Relapse (Psp) Prp*4 0.3488 0.3139–0.3837 Beta Remission → Remission (Prr) 1-Prp 0.9128 0.8215-1.0000 Beta Remission → Relapse (Prp) 1-exp(-0.75*In(2)/(DOR)) 0.0872 0.0785–0.0959 Beta Relapse → Relapse (Ppp) 1-Ppd 0.9661 0.8695-1.0000 Beta Relapse →Death (Ppd) 1-exp(-0.75*In(2)/(OS-PFS)) 0.0339 0.0305–0.0373 Beta Discount rate for costs and QALYs 5% per year Health state utilities No recurrence (chemotherapeutic period) 0.74 0.592–0.888 Beta No recurrence (after chemotherapy) 0.94 0.752-1.000 Beta local recurrence (in the first year) 0.74 0.592–0.888 Beta remission 0.85 0.68-1.000 Beta Relapse 0.5 0.4–0.6 Beta OS overall survival, DOR duration of response, PFS progression free survival Model inputs for costs All drug fees come from Hangzhou First People's Hospital in China. The cost of treatment is calculated based on the patient's weight and body surface area. In the Markov model, we allocate the costs attributable to chemotherapy to the first cycle. Sensitivity analysis The probabilistic sensitivity analysis adopts the Monte Carl simulation method, each simulation generates a Markov cohort of 10,000 people, a total of 1,000 simulations, 1,000 Markov cohorts can be obtained, and the ICER of the two chemotherapy regimens for metastatic breast cancer can be compared with each other after calculation, and then the probabilistic sensitivity analysis can be performed. All model parameters have random values based on their individual distribution. The uncertainty of the model results is comprehensively estimated by randomly sampling all input parameters of the model at the same time(van Nuland et al. 2018 ). The beta-distribution is used to express the uncertainty of utility, probability, and proportion, and the gamma-distribution is used for cost data(see Table 2 ). Based on the results of PSA, we plotted a cost-effectiveness acceptability curve to show the proportion of different levels of cost- effectiveness t simulation. In addition, we performed a one-way sensitivity analyses to examine the influence of relevant variables on the results of the cost-effectiveness analysis. Results Base‑case treatment analyses In our model, we calculated the lifetime cumulative costs, quality adjusted life years(QALYs), incremental QALYs, incremental costs, incremental cost-effectiveness ratios(ICERs), and incidences of death for the five treatment strategies(Table 2 ). The Markov model calculated that the four distribution proportions of the VEX program were stabilization (0.1%), remission (16.8%), relapse (73.1%) and death (10%). Additionally, the four distribution proportions of the P program were stabilization (0.3%), remission (18.8%), relapse (69.7%) and death (11.2%)(see Fig. 2 ). The Markov model predicted that the total costs of a 5-year horizon incurred with the VEX and P regimen were $ 74617.32 and $ 6020.53, respectively. Moreover, VEX gained an additional 0.4 QALYs compared with P. As a result, the ICER of VEX is $ 40 333.69/QALY, which is higher than the WTP threshold of $ 37 653.0/QALY. The ICER of P is $ 4 152.09/QALY, which is less than the WTP threshold of $ 37 653.0/QALY. This indicates that P regimen is cost-effective when compared with VEX regimen. The results of the base case analysis are shown in Table 2 . The economic outcomes of alternative strategies are presented in Fig. 3 . The VEX strategy is the dominant strategy. Table 2 Cost and effect of ACTH and TCH within five years Item Status VEX P Deviation Effect Disease Free Survival/% 16.8% 18.8% -2 Death/% 10% 11.2% -1.2 QALYs 1.85 1.45 0.4 Costs ( $ ) 74617.32 6020.53 68596.79 ICER( $ /QALY) 40333.69 4152.09 171491.97 General safety The most common AEs (any grade) during the neoadjuvant period were anemia, infection, anorexia, diarrhea, constipation, nausea, fatigue and neutrophil count decreased (Table 3 ). The most common grade 3–4 AEs were fatigue, anemia and neutrophil count decreased (Table 3 ). Fatigue was reported in 5 patients (7.1%) in the VEX group and 1 patient (1.6%) in the P group. The incidence of peripheral sensory neuropathy was also higher in P Group, with 5 patients (7.9%). The most common serious AEs were neutral count decreased. 20 patients (28.6%) in the VEX group had neutral count decreased, while 9 patients (14.3%) in the P group had neutral count decreased. No grade 5 AEs were reported(Insinga et al. 2018 ; Wu et al. 2020 ). Table 3 Adverse events (G1-2 and G3-4) for VEX vs P VEX Adverse events (n = 70) P Adverse events (n = 63) G1-2 G3-4 G1-2 G3-4 Anemia 25(35.7) 4(5.7) 41(65%) 2(3.2) Alopecia 2(2.9) NR 21(33.4) NR Allergic reaction 2(2.9) NR 6(9.5) 1(1.6) Anorexia 5(7.1) 1(1.4) 1(1.6) NR Diarrhea 22(31.4) 1(1.4) 12(19) NR Constipation 6(8.6) NR 13(20.6) NR Nausea 28(40) 2(2.9) 17(26.9) NR Peripheral sensory neuropathy 4(5.7) NR 30(47.6) 5(7.9) Infection 11(15.8) 2(2.9) 19(30.1) 1(1.6) Fatigue 29(41.4) 5(7.1) 34(54.0) 1(1.6) Neutrophil count decreased 9(12.9) 20(28.6) 12(19.1) 9(14.3) Sensitivity analyses Single‑factor sensitivity analysis This study calculated the corresponding transmission probability based on relevant literature data, and applied the maximum and minimum values in the literature data to calculate the corresponding transmission probability values as the scope of sensitivity analysis. The drug prices, incidence of adverse reactions, and cost of adverse reactions in this study are all sourced from real data, while health utility data is sourced from literature. Therefore, sensitivity analysis method is adopted to perform single factor sensitivity analysis on all parameters of the input Markov model. The results were summarized in a tornado diagram (Fig. 4 ). It is not difficult to see from the graph that every transition probability that changes within its sensitivity range will more or less affect the final conclusion. Therefore, transition probabilities are important variables that affect the conclusion of the model. The six parameters that have the greatest impact are: the relapse transition probability of P regimen (PPpd), the transition probability of P regimen from remission to remission (Prr), the stable transition probability of P regimen (PPsd), the transition probability of P regimen from stable to stable (Pss), the transition probability of P regimen from stabilization to remission (Psr), and the transition probability of P regimen from relapse to death (Ppd). Nevertheless, other parameters, including drug costs and other disease utilities, had little impact on the robustness of the model. As the health utility value in this study only comes from foreign literature, this parameter was unstable. However, after sensitivity analysis of each parameter, it was found that the transfer probability of relapse had the greatest impact on the model results among the two chemotherapy regimens. Results of probability sensitivity analysis Probability sensitivity analysis adopts Monte Carlo simulation method. Each simulation will generate a Markov queue of 10000 people. The total number of Markov queues is 1000. After calculation, the ICERs of neoadjuvant chemotherapy for two kinds of breast cancer are compared. In addition, based on the Monte Carlo simulation results, we compared the scatter plots of the probability sensitivity analysis between the VEX regimen and the P regimen. Among them, the horizontal and vertical axes represent the incremental effect and incremental cost of the VEX regimen relative to the P regimen, respectively. Each scatter in the figure represents the VEX regimen. The incremental cost-effectiveness ratio (ICER) is shown in the figure. Most of the scattered points in the figure are distributed in the first quadrant, and ICER ratio is generally positive. This means that the ICERs of 10000 Monte Carlo simulations are all above the WTP threshold, indicating that the VEX regimen does not have a cost-effectiveness advantage compared to the P regimen (see Fig. 5 ). For a cohort of 1000 breast cancer patients, the cost-effectiveness acceptable curve can be derived from the mutual comparison of the two regimens. It can be seen from Fig. 5 that within the WTP range, the probability of the VEX regimen has a cost effect which is close to 0, and the P regimen acceptance curve is maintained at a high level, which indicates that the P regimen has the highest acceptable probability and is the preferred solution(see Fig. 6 ). Discussion At present, chemotherapy is still an important method for clinical treatment of patients with metastatic breast cancer, but most of the traditional chemotherapy programs use "maximum tolerated dose (MTD)" to treat patients. Although MTD has a good killing effect on tumor cells, due to its high toxicity, many patients have adverse reactions, and some patients even terminate treatment because they can’t tolerate toxic side effects. Moreover, in traditional chemotherapy, there is a long interval between two chemotherapy cycles, which can easily cause tumor cells to regrow, disease progression, and even lead to tumor cell resistance(Kerbel 2015 ). In recent years, the metronomic chemotherapy model that uses low-dose, low toxicity, and continuous application of anti-tumor drugs to inhibit tumor angiogenesis and promote tumor cell apoptosis has received much attention(Di Desidero et al. 2015 ). Metronomic chemotherapy has better anti angiogenic effects and does not increase toxic side effects(Cazzaniga et al. 2016 ). Taking Vinorelbine as an example, previous basic research has shown that compared to traditional chemotherapy, Vinorelbine metronomic chemotherapy can significantly downregulate the expression of pro angiogenic genes and upregulate the expression of antiangiogenic genes(Biziota et al. 2016 ). In addition, the study by Mavroeidis L et al. confirmed that the blood drug concentration of Vinorelbine metronomic chemotherapy can effectively inhibit the proliferation of vascular endothelial cells(Mavroeidis et al. 2015 ). Vitro experiments have shown that metronomic chemotherapy combined with antiangiogenic drugs can effectively reduce tumor stem cell generation(Vives et al. 2013 ; Folkins et al. 2007 ). The METEORA-II trial showed that compared with weekly intravenous paclitaxel chemotherapy, oral vinorelbine + cyclophosphamide + capecitabine metronomic chemotherapy significantly prolonged TTF and PFS, and had a higher disease control rate(Munzone et al. 2023 ). The OS of the two groups was similar. Although the toxic reactions of VEX increased, they were basically controllable. However, in the adjuvant treatment of metastatic breast cancer, patients bear a great financial burden. The economic evaluation of postoperative adjuvant therapy is crucial for maintaining a balance between clinical benefits and medical costs, especially in developing countries such as resource- limited China(Huang et al. 2018 ). Therefore, we have established a cost- effectiveness analysis of vinorelbine + cyclophosphamide + capecitabine (VEX regimen) and weekly intravenous paclitaxel (P regimen) for the treatment of metastatic breast cancer patients in China. This is the first time to analyze ER+/HER2 adjuvant treatment strategies for metastatic breast cancer from the perspective of efficacy and cost-effectiveness. Different countries use different WTP thresholds for medical cost-effectiveness analysis. The cost effectiveness threshold range recommended by the United States is $ 50000 to $ 200000 per QALY(Neumann, Cohen, and Weinstein 2014 ). Although China has not set a cost-effectiveness threshold, the Chinese Pharmacoeconomic Evaluation Guidelines suggest that if the ICER is below the threshold of per capita GDP (approximately $ 34240 in 2015), the additional cost of new therapies is worthwhile; If the ICER is between three times the per capita GDP and per capita GDP, the additional cost of the new therapy is acceptable; If the ICER is greater than three times the per capita GDP, then the additional cost of this new therapy is not worth it. In China, the most widely used cost-effectiveness threshold when evaluating new therapies is three times the per capita GDP(Liao et al. 2019 ). In our analysis, the ICER of the VEX regimen is $ 40 333.69/QALY, which is higher than per capita GDP but much lower than three times per capita GDP. Therefore, according to the Chinese Pharmacoeconomic Guidelines, VEX is acceptable as a treatment for advanced breast cancer. The ICER of P regimen is $ 4 152.09/QALY, which is lower than the per capita GDP. Although the VEX regimen improves patient TTF and PFS, it requires significant additional costs. Even with additional sensitivity analysis indicating that the VEX regimen improves AEs management, we cannot determine whether the high cost of the VEX regimen is reasonable. Therefore, in China, the P regimen may be a more cost-effective option. The high prices of anti-cancer drugs have led to a sharp increase in the consumption of medical resources, which has troubled clinical doctors and medical managers. The healthcare expenditures of high-income countries such as the United States and Europe have been increasing year by year, and they have explored potential methods for pricing cancer treatment drugs, hoping to maintain a sustainable impact on healthcare systems(Godman et al. 2021 ). Low and middle-income countries are also actively addressing the issue of rising cancer treatment costs. Especially with the diversification of cancer treatment methods, choosing new anti-tumor drugs such as Vinorelbine may not be cost-effective in low and middle-income countries(Gershon et al. 2019 ). Therefore, it is of great significance to evaluate treatment plans economically and make reasonable and effective use of limited medical resources. However, to our knowledge, China's medical insurance has already covered as many people as possible. Although there are significant differences in the reimbursement ratios of different regions and types of medical insurance, choosing a treatment plan with better results and lower costs is beneficial for the rational use of medical insurance. Therefore, we believe that properly increasing the reimbursement proportion of VEX regimen in patients with metastatic breast cancer will help to save medical expenses and benefit more patients. In general, from the perspective of Chinese payers, P regimen is a cost-effective strategy for first-line systemic treatment of metastatic breast cancer. Due to data availability and model assumptions, our economic model has some limitations. Firstly, the clinical data comes from the METEORA-II trial(Munzone et al. 2023 ), where the majority of patients were from Italy and the proportion of Asians was small and unavailable, which may have a slight impact on our results. Due to the lack of research results on the health effects of breast cancer in different states in China, this limitation cannot be avoided at present. Updating the clinical data of metastatic breast cancer patients in the Chinese population may improve the accuracy in the future. Secondly, in this study, we assume that the transition probability of the Markov model remains unchanged during the study period. However, in the actual processing process, the transition probability between different states varies over time. Thirdly, the incidence of adverse reactions in different disease states of breast cancer is different, and the incidence of adverse reactions generally changes with time. The clinical real data relied on by this study only provides the incidence of adverse reactions for the entire treatment group, and does not provide the incidence of adverse reactions for different disease states. Our study also did not discuss the decrease in utility value caused by adverse effects. However, sensitivity analysis indicates that changes in utility values do not alter the results in terms of quality. Fourthly, in our analysis, we did not consider indirect costs such as income loss caused by discontinuation of treatment and premature death, as the high variability of the condition makes it difficult to accurately calculate. Fifthly, drug prices and treatment costs are derived from previously published literature or local data and may not be applicable to all regions. To avoid the impact of these costs on the model, we changed the treatment costs within a considerable range (± 20%) in the one-way sensitivity analysis. Variation is mediated by these factors and is limited. Although our research has certain limitations, after sensitivity analysis, these variables do not affect the final results. Conclusion According to traditional economic evaluation methods, the chemotherapy cost of P regimen is lower than that of VEX regimen. Although the TTF and PFS of the VEX regimen are higher than those of the P regimen in terms of efficacy, and the disease control rate of the VEX regimen is also higher than that of the P regimen, this requires a significant additional cost, and the OS of the two regimens is ultimately similar. In addition, the analysis of incremental cost-effectiveness shows that with VEX as the reference group, P regimen. is the preferred option. Therefore, considering both cost and effectiveness, compared to the VEX regimen, the P regimen is a cost-effective option, and at the current price, the P regimen may benefit more ER+/HER2- metastatic breast cancer women in China. We hope that our research can provide references for doctors or patients when making treatment decisions. Declarations Acknowledgements We are grateful to the teachers of Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou. Author contributions NR and QPX were major contributors to writing the manuscript. LQR drew the schematic diagram, JJP and YBY completed the literature search. Funding This study was funded by Key Medical Discipline of Hangzhou City (2021–21); Key Medical Discipline of Zhejiang Province (2018–2–3); Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province (2020E10021); Zhejiang Province Medical and Health Science and Technology Program (2023KY933); Zhejiang Traditional Chinese Medicine Science and Technology Project (2023ZL565). Availability of data and materials Not applicable. Ethics approval and consent to participate Not applicable. Consent for publication All authors are in agreement with the content for publication. Competing interests The authors declare that they have no competing interest. References 《中国药物经济学评价指南》课题组 (2011) 刘国恩, 胡善联, and 吴久鸿. 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Cost Eff Resour Alloc 17:5 Godman B, Hill A, Simoens S, Selke G, Selke Krulichová I, Zampirolli Dias C, Martin AP, Oortwijn W, Timoney A, Gustafsson LL, Voncina L, Kwon HY, Gulbinovic J, Gotham D, Wale J, Silva WCD, Bochenek T, Allocati E, Kurdi A, Ogunleye OO, Meyer JC, Hoxha I, Malaj A, Hierländer C, Sauermann R, Hamelinck W, Petrova G, Laius O, Langner I, Yfantopoulos J, Joppi R, Jakupi A, Greiciute-Kuprijanov I, Vella Bonanno P, Piepenbrink JH, de Valk V, Wladysiuk M, Marković-Peković V, Mardare I, Fürst J, Tomek D, Obach Cortadellas M, Zara C, Pontes C, McTaggart S, Laba TL, Ø, Melien D, Wong-Rieger S, Bae, Hill R (2021) 'Potential approaches for the pricing of cancer medicines across Europe to enhance the sustainability of healthcare systems and the implications', Expert Rev Pharmacoecon Outcomes Res , 21: 527 – 40 Gogate A, Rotter JS, Trogdon JG, Meng K, Baggett CD, Katherine E, Reeder-Hayes, Wheeler SB (2019) An updated systematic review of the cost-effectiveness of therapies for metastatic breast cancer. Breast Cancer Res Treat 174:343–355 Huang J, Liao W, Zhou J, Zhang P, Wen F, Wang X, Zhang M, Zhou K, Wu Q, Li Q (2018) Cost-effectiveness analysis of adjuvant treatment for resected pancreatic cancer in China based on the ESPAC-4 trial. Cancer Manage Res 10:4065–4072 Huang, Yuan Q, Li S, Torres-Rueda, Li J (2020) The Structure and Parameterization of the Breast Cancer Transition Model Among Chinese Women. Value in Health Regional Issues 21:29–38 Insinga RP, Vanness DJ, Feliciano JL, Vandormael K, Traore S, Burke T (2018) Cost-effectiveness of pembrolizumab in combination with chemotherapy in the 1st line treatment of non-squamous NSCLC in the US. J Med Econ 21:1191–1205 Kerbel RS (2015) A Decade of Experience in Developing Preclinical Models of Advanced- or Early-Stage Spontaneous Metastasis to Study Antiangiogenic Drugs, Metronomic Chemotherapy, and the Tumor Microenvironment. Cancer J 21:274–283 Liao M, Jiang Q, Hu H, Han J, She L, Yao L, Ding D, Huang J (2019) Cost-effectiveness analysis of utidelone plus capecitabine for metastatic breast cancer in China. J Med Econ 22:584–592 Mavroeidis L, Sheldon H, Briasoulis E, Marselos M, Pappas P, Harris AL (2015) Metronomic vinorelbine: Anti-angiogenic activity in vitro in normoxic and severe hypoxic conditions, and severe hypoxia-induced resistance to its anti-proliferative effect with reversal by Akt inhibition. Int J Oncol 47:455–464 Montagna E, Bagnardi V, Cancello G, Sangalli C, Pagan E, Iorfida M, Mazza M, Mazzarol G, Dellapasqua S, Munzone E, Goldhirsch A, Colleoni M (2018) Metronomic Chemotherapy for First-Line Treatment of Metastatic Triple-Negative Breast Cancer: A Phase II Trial. Breast Care (Basel) 13:177–181 Munzone E, Regan MM, Cinieri S, Montagna E, Orlando L, Shi R, Campadelli E, Gianni L, Palleschi M, Petrelli F, Bengala C, Generali D, Collovà E, Puglisi F, Cretella E, Zamagni C, Chini C, Ruepp B, Loi S, Colleoni M (2023) 'Efficacy of Metronomic Oral Vinorelbine, Cyclophosphamide, and Capecitabine vs Weekly Intravenous Paclitaxel in Patients With Estrogen Receptor-Positive, ERBB2-Negative Metastatic Breast Cancer: Final Results From the Phase 2 METEORA-II Randomized Clinical Trial', JAMA Oncol Neumann PJ, Cohen JT, Weinstein MC (2014) Updating cost-effectiveness–the curious resilience of the $ 50,000-per-QALY threshold. N Engl J Med 371:796–797 Paracha N, Thuresson P-O, Moreno SG, MacGilchrist KS (2016) Health state utility values in locally advanced and metastatic breast cancer by treatment line: a systematic review. Expert Rev PharmacoEcon Outcomes Res 16:549–559 Rosswag S, Cotarelo CL, Pantel K, Riethdorf S, Sleeman JP, Schmidt M, Thaler S (2021) 'Functional Characterization of Circulating Tumor Cells (CTCs) from Metastatic ER+/HER2- Breast Cancer Reveals Dependence on HER2 and FOXM1 for Endocrine Therapy Resistance and Tumor Cell Survival: Implications for Treatment of ER+/HER2- Breast Cancer'. Cancers (Basel), 13 Rui M, Shi F, Shang Y, Meng R, Li H (2020) Economic Evaluation of Cisplatin Plus Gemcitabine Versus Paclitaxel Plus Gemcitabine for the Treatment of First-Line Advanced Metastatic Triple-Negative Breast Cancer in China: Using Markov Model and Partitioned Survival Model. Adv Therapy 37:3761–3774 Seki H, Sakurai T, Maeda Y, Oki N, Aoyama M, Yamaguchi R, Tokuda T, Kaburagi T, Okumura T, Karahashi T, Nakajima K, Higeta K, Shimizu K (2019) 'Efficacy and Safety of Palbociclib and Fulvestrant in Japanese Patients With ER+/HER2- Advanced/Metastatic Breast Cancer', In Vivo , 33: 2037-44 Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209–249 van Nuland M, Vreman RA, ten Ham RMT, de Vries Schultink AHM, Rosing H, Schellens JHM, Beijnen JH, Hövels AM (2018) Cost-effectiveness of monitoring endoxifen levels in breast cancer patients adjuvantly treated with tamoxifen. Breast Cancer Res Treat 172:143–150 Vives M, Ginestà MM, Gracova K, Graupera M, Casanovas O, Capellà G, Serrano T, Laquente B, Viñals F (2013) Metronomic chemotherapy following the maximum tolerated dose is an effective anti-tumour therapy affecting angiogenesis, tumour dissemination and cancer stem cells. Int J Cancer 133:2464–2472 Ward MC, Vicini F, Al-Hilli Z, Chadha M, Pierce L, Recht A, Hayman J, Thaker N, Khan AJ, Keisch M, Chirag Shah (2020) Cost-effectiveness analysis of endocrine therapy alone versus partial-breast irradiation alone versus combined treatment for low-risk hormone-positive early-stage breast cancer in women aged 70 years or older. Breast Cancer Res Treat 182:355–365 Ward S, Simpson E, Davis S, Hind D, Rees A, Wilkinson A (2007) Taxanes for the adjuvant treatment of early breast cancer: systematic review and economic evaluation. Health Technol Assess 11:1–144 Wu Q, Liao W, Zhang M, Huang J, Zhang P, Li Q (2020) 'Cost-Effectiveness of Tucatinib in Human Epidermal Growth Factor Receptor 2–Positive Metastatic Breast Cancer From the US and Chinese Perspectives'. Front Oncol, 10 Yeo W, Ueno T, Lin CH, Liu Q, Lee KH, Leung R, Naito Y, Park YH, Im SA, Li H, Yap YS, Lu YS, Group Asian Breast Cancer Cooperative (2019) Treating HR+/HER2- breast cancer in premenopausal Asian women: Asian Breast Cancer Cooperative Group 2019 Consensus and position on ovarian suppression. Breast Cancer Res Treat 177:549–559 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3860294","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268075408,"identity":"7570c4f8-c9a9-416b-a3fe-3a626cd49c74","order_by":0,"name":"Ning Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Ren","suffix":""},{"id":268075409,"identity":"0fdd2e47-caaf-467c-8a91-5b8b4761f856","order_by":1,"name":"Qiaoping Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYJCCAx8q2OTY2JsPEK2D8eCMM3zGfDzHEojWwnyYt00ucZ5EjgJx6g2O5x44zHPGLL2NIYeB4UfFNsJaJHveJRycU5GW28Zw9gBjz5nbhLXwS+QYHHhz5lhuG2NfAjNjGxFa2EBaeNv+p7Mx8xgQpwVky0HeNrYENjZitYD9MuMMm2EbD1vCQaL8Agyxwx+AUSkvP//xwQc/KojQwgAKWxg4QIx6VC2jYBSMglEwCrACALJEP0fYe/bKAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Qiaoping","middleName":"","lastName":"Xu","suffix":""},{"id":268075410,"identity":"30f653f2-cf9e-47ac-bea8-456bfb2f793e","order_by":2,"name":"Lanqi Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lanqi","middleName":"","lastName":"Ren","suffix":""},{"id":268075411,"identity":"e33164b1-a9f7-4d64-8ece-bb002a0bd0e2","order_by":3,"name":"Yibei Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yibei","middleName":"","lastName":"Yang","suffix":""},{"id":268075412,"identity":"477a7159-819a-4d37-8df9-e3bf371692a9","order_by":4,"name":"Junjie Pan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2024-01-13 13:44:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3860294/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3860294/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50058950,"identity":"54e769a6-2a1b-4f2b-a91a-6476db6a9a85","added_by":"auto","created_at":"2024-01-23 19:02:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82600,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the Decision Tree and Markov Model (a and b). VEX Vinorelbine plus Cyclophosphamide and Capecitabine, P Paclitaxel.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/8967ad8d4d6437e354182bdd.png"},{"id":50058948,"identity":"0fd6bcff-3aa9-434c-998c-dafc010f63c0","added_by":"auto","created_at":"2024-01-23 19:02:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99058,"visible":true,"origin":"","legend":"\u003cp\u003eMarkov cohort analysis (A)VEX cohort (B) P cohort. These curves show the output of VEX and P models. The horizontal axis shows time(years) and the vertical axis shows the proportion of people\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/b9bff11fe171d59d7af2e4a3.png"},{"id":50058947,"identity":"cd281b39-e48e-4a72-a2c0-fab44035ebd0","added_by":"auto","created_at":"2024-01-23 19:02:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39734,"visible":true,"origin":"","legend":"\u003cp\u003eResults of cost-effectiveness analyses for the Breast cancer patients. The vertical axes represent the lifetime cumulative cost and the horizontal axes represent the quality-adjusted life years (QALYs) gained. The VEX strategy is the dominant strategy.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/f9f689af59e4e80153931972.jpg"},{"id":50058949,"identity":"9c5a3a85-7233-4376-9f54-612b77d47d5b","added_by":"auto","created_at":"2024-01-23 19:02:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107062,"visible":true,"origin":"","legend":"\u003cp\u003eTornado diagram representing the cost per QALY gained in one-way sensitivity analysis for VEX strategy versus P strategy. The width of the bars represents the range of the results when the variables were changed. The width of the bars represents the range of results when the variables are changed. The vertical dotted line represents the base-case results.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/8edaea4a0aff955e32a0d9e8.jpg"},{"id":50058952,"identity":"d219cb9c-a264-4ac4-9f2d-74b88b770a8e","added_by":"auto","created_at":"2024-01-23 19:02:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157366,"visible":true,"origin":"","legend":"\u003cp\u003eProbabilistic results of the incremental cost-effectiveness differences between treatment with VEX and P for a cohort of 1000 Breast cancer patients.The vertical axes representthe incremental costs.The horizontal axes represent the incremental quality-adjusted life years (QALYs) gained. The diagonal line represent the willingness-to-pay(WTP).\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/94f168c336d767bc66ec7fa4.jpg"},{"id":50058951,"identity":"db1425ee-54f7-48ff-b27c-326f5b927e41","added_by":"auto","created_at":"2024-01-23 19:02:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78718,"visible":true,"origin":"","legend":"\u003cp\u003eCost-effectiveness acceptability curves showing the probabilities of net benefits achieved by each strategy for different willingness-to-pay thresholds (the maximum amount a person would be willing to pay for a good) in VEX (a) and P (b) cohorts. The vertical axes represent the probability of cost-effectiveness. The horizontal axes represent willingness-to-pay thresholds to gain one additional quality-adjusted life year (QALY).\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/3c625ffffe6b36c281af6579.jpg"},{"id":50215412,"identity":"3ecee072-6cd2-4502-85b3-0afb8edba8da","added_by":"auto","created_at":"2024-01-26 13:37:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":791219,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3860294/v1/19b7a401-f7b3-406d-9236-f88a6da097b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-effectiveness of Metronomic chemotherapy vs Weekly Intravenous Paclitaxel in patients with ER+/HER2-Metastatic Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn most countries, breast cancer remains the most diagnosable cancer and the leading cause of cancer deaths in women worldwide. It was estimated that there were approximately 2.3\u0026nbsp;million patients with newly diagnosed breast cancer, accounting for 11.7% of all new cancer cases, and more than 680 000 deaths worldwide in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer(Sung et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The incidence of breast cancer in Asia has escalated over recent decades, especially among younger women(Yeo et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The incidence rate in China also shows an increasing trend year by year.\u003c/p\u003e \u003cp\u003eAround 70% of breast cancers express the estrogen receptor alpha (ERα) and depend on estrogen for growth and disease progression. Although most ER-positive/HER2-negative (ER+/HER2\u0026minus;) breast cancers are associated with good prognosis, around one third of patients progress and develop late recurrences, sometimes even decades after initial diagnosis and treatment(Rosswag et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Endocrine therapy for estrogen receptor/human epidermal growth factor receptor 2 (ER+/HER2\u0026ndash;) tumors has greatly contributed to reduce early breast cancer recurrence. However, resistance towards endocrine therapies occurs often, and is found in nearly all hormone receptor positive breast cancer patients with metastatic disease. Metastatic breast cancer (MBC) treatment remains a significant clinical issue(Seki et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn clinical practice, most patients with ER+/HER2- metastatic breast cancer will receive chemotherapy. In order to improve the efficacy of chemotherapy, the maximum tolerable dose of cytotoxic drugs is often used as the standard dose for treatment. However, due to the high toxicity, patients cannot tolerate it for a long time, which limits its long-term use in clinical practice. Metronomic chemotherapy is an emerging treatment mode in recent years, which uses low-dose chemotherapy drugs equivalent to conventional doses of 1/10\u0026thinsp;\u0026minus;\u0026thinsp;1/3, continuously or high-frequency (1\u0026ndash;3 times a week) administration, targeting activated endothelial cells within the tumor as the treatment target. This approach significantly reduces toxicities and the need for growth factor support to accelerate recovery from myelosuppression(Montagna et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared to traditional chemotherapy, metronomic chemotherapy has its unique mechanism of action, which is to shorten the treatment interval, on the one hand, reduce the repair of tumor cells during the treatment interval, and achieve direct tumor inhibition; On the other hand, it exerts anti-tumor effects through anti-tumor angiogenesis and immune regulation. At the same time, the dosage of metronomic chemotherapy is relatively low, which can maintain a relatively low but effective blood drug concentration for a long time, in order to prolong disease control time and reduce toxic side effects. Therefore, it has the advantages of low adverse reactions, low drug resistance, low treatment costs, and easy long-term administration. Based on the characteristics of metronomic chemotherapy, oral medication is generally preferred.\u003c/p\u003e \u003cp\u003eThe metronomic combination of capecitabine, vinorelbine, and cyclophosphamide (VEX regimen) was recently investigated in a phase II trial of patients with endocrine-responsive MBC. METEORA-II trial is a multicenter, open label, randomized controlled phase II clinical trial to evaluate the efficacy and safety of vinorelbine\u0026thinsp;+\u0026thinsp;Cyclophosphamide\u0026thinsp;+\u0026thinsp;Capecitabine (VEX regimen) in ER+/HER2 metastatic breast cancer patients(Munzone et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study included a total of 140 patients who were randomly divided into the VEX group and the P group. 133 patients were included in the efficacy analysis set, including 70 patients in the VEX group and 63 patients in the P group. At the final analysis of the study, the median follow-up time was 29 months, with 128 TTF events and 61 deaths among 133 patients. Compared with the P group, the VEX group showed significant improvements in TTF and PFS: median TTF was 8.3 months and 5.7 months, median PFS was 11.1 months and 6.9 months, and the 12 months PFS rates were 43.5% and 21.9%, respectively. There was no significant difference in OS between the two groups. The METEORA-II trial showed that VEX metronomic chemotherapy significantly increased TTF compared to single paclitaxel P, and for ER+/HER2-MBC patients who plan chemotherapy, the metronomic chemotherapy VEX regimen can be considered as a first-line chemotherapy regimen. Therefore, based on the METEORA-II trial, we evaluated the cost-effectiveness of the full oral rhythm chemotherapy regimen vinorelbine\u0026thinsp;+\u0026thinsp;Cyclophosphamide\u0026thinsp;+\u0026thinsp;Capecitabine (VEX regimen) and weekly intravenous paclitaxel (P).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatement\u003c/h2\u003e\n \u003cp\u003eAccording to the ministry of health \u0026quot;measures on ethical review of biomedical research involving human beings (trial 2007)\u0026quot;、WMA《Declaration of Helsinki》\u003c/p\u003e\n \u003cp\u003eand the ethical principles of CIOMS《the international moral guide to human biological research》,Subject to review by the ethics committee, Agree to carry out this study according to the research regimen under review.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eDesign and structure of the model\u003c/h2\u003e\n \u003cp\u003eA decision- analytic Markov model was developed using TreeAge Pro Suit 2011 to analyze the cost-effectiveness of VEX metronomic chemotherapy or weekly intravenous paclitaxel for ER+/HER2- metastatic breast cancer patients. After the specified treatment, the patient will be in one of the following four health states: remission, stability, relapse and death, as shown in the model in Fig. 1. Each circle in Fig. 1 represents a state, and the arrows represent transitions between states. A patient can move from a healthy state to a disease state and from a disease state to a healthy state. The patient may die in either a healthy state or a state of illness, that is, transfer to a state of death. We set the utility weight and cost of each cycle for each health state, and estimate the movement between each health state according to the conversion probability calculated in clinical trials. The cycle length is one year, and it is adjusted to half cycle in each health state process(Huang et al. 2020; Rui et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWe calculated the change in the proportion of patients present in each state over time, as well as the cumulative cost and quality-adjusted life years (QALYs) of patients in a certain state of health for each period of time. A quality-adjusted life year (QALY) is estimated as the time spent in each health state multiplied by the utility associated with the health state(Gogate et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Austin et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The discount rate is an important parameter in pharmacoeconomic evaluation and is a parameter of the time value of the reaction cost used in the calculation of cost equivalence. According to the Chinese Pharmacoeconomic Evaluation Guidelines (2011 edition)(《中国药物经济学评价指南》课题组 et al. 2011),the internationally recommended 5% discount rate was selected in our study to discount the cost and quality years. In the baseline analysis, the cost is calculated using a 5% discount rate, and in the sensitivity analysis, the incremental cost utility ratio is calculated using a 0% -5% discount rate to examine the stability of the results. The incremental cost-effectiveness ratio (ICER) is defined as a ratio of incremental costs to incremental benefits, and we represent the results in ICERs based on the cost of each QALY obtained(Ward et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In order to conduct probabilistic sensitivity analysis (PSA), we used second-order Monte Carlo analysis (1000 simulations) to simulate the model and constructed confidence intervals using bootstrap replications (1000 replications).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003ePatients and treatment plans\u003c/h2\u003e\n \u003cp\u003eThe METEORA-II trial enrolled 140 female patients with ER+/HER2- metastatic breast cancer whose age was \u0026ge;\u0026thinsp;18 years from 15 medical centers in Italy between September 13, 2017, and January 14, 2021. Patients could receive 1-line chemotherapy and/or 2-line endocrine therapy in the past, including CDK4/6 inhibitors. Patients were randomly divided into two groups in a 1:1 ratio: the VEX group and the paclitaxel group. VEX group: Take orally 40 mg vinorelbine, 50 mg Cyclophosphamide and 500 mg Capecitabine three times a day on the first, third and fifth days of each week until the disease progresses or is intolerable. Paclitaxel group: Intravenous injection of paclitaxel (90 mg/m\u003csup\u003e2\u003c/sup\u003e) every 4 weeks on days 1, 8, and 15 until disease progression or intolerance(Montagna et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eModel inputs for transition probabilities\u003c/h2\u003e\n \u003cp\u003eThe transition probability is the probability that an event will occur in a specific period of time, and the transition probability in Markov\u0026apos;s model is the probability of being in a different state of health during the cycle. In this study, the DEALE method was used to calculate the transition probability by using the data of the phase II clinical trial(Munzone et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the transition probability calculation process is shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In addition, we converted all hazard ratios and survival rates into transition probabilities for one-year time periods. The log-logistic model is suitable for describing PFS and OS, and we calibrate the transition probability from the actual overall lifetime (OS) data and median time to progression and feed it into the Markov model(Diaby et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Use the chi-square test to compare the differences between the actual OS data and the model data derived from our Markov state.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eModel inputs for effectiveness\u003c/h2\u003e\n \u003cp\u003eHealth utility index refers to the weight of different health states relative to complete health in all health states, which is an index for evaluating the satisfaction of a certain health status, and is a comprehensive index reflecting the health status of individuals. Each state of health utility index is between 0\u0026ndash;1, with 0 representing death and 1 being fully healthy. The health utility values for this study were derived from the health utility index used in previously published analyses(Ward et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Paracha et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. These utilities were chosen because their settings are similar.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey parameters for the model of adjuvant treatment of regional breast cancer with VEX regimen and P regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBest estimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSA range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--VEX\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable \u0026rarr; Stable (VEXss)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-VEXsp-VEXsr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5128\u0026ndash;0.6268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable \u0026rarr; Remission (VEXsr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-exp(-RR/3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1690\u0026ndash;0.2066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable \u0026rarr; Relapse (VEXsp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVEXrp*4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2182\u0026ndash;0.2666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemission \u0026rarr; Remission (VEXrr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-VEXrp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8455-1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemission \u0026rarr; Relapse (VEXrp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-exp(-0.75*In(2)/(DOR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0545\u0026ndash;0.0667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse \u0026rarr; Relapse (VEXpp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-VEXpd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8749-1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse \u0026rarr; Death (VEXpd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-exp(-0.75*In(2)/(OS-PFS))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0251\u0026ndash;0.0307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--P RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable \u0026rarr; Stable (Pss)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-Psp-Psr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3765\u0026ndash;0.4601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable \u0026rarr; Remission (Psr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-exp(-RR/3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2096\u0026ndash;0.2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable \u0026rarr; Relapse (Psp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrp*4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3139\u0026ndash;0.3837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemission \u0026rarr; Remission (Prr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-Prp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8215-1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemission \u0026rarr; Relapse (Prp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-exp(-0.75*In(2)/(DOR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0785\u0026ndash;0.0959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse \u0026rarr; Relapse (Ppp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-Ppd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8695-1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse \u0026rarr;Death (Ppd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-exp(-0.75*In(2)/(OS-PFS))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0305\u0026ndash;0.0373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscount rate for costs and QALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5% per year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth state utilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo recurrence (chemotherapeutic period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.592\u0026ndash;0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo recurrence (after chemotherapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elocal recurrence (in the first year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.592\u0026ndash;0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eremission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOS overall survival, DOR duration of response, PFS progression free survival\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eModel inputs for costs\u003c/h2\u003e\n \u003cp\u003eAll drug fees come from Hangzhou First People\u0026apos;s Hospital in China. The cost of treatment is calculated based on the patient\u0026apos;s weight and body surface area. In the Markov model, we allocate the costs attributable to chemotherapy to the first cycle.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n \u003cp\u003eThe probabilistic sensitivity analysis adopts the Monte Carl simulation method, each simulation generates a Markov cohort of 10,000 people, a total of 1,000 simulations, 1,000 Markov cohorts can be obtained, and the ICER of the two chemotherapy regimens for metastatic breast cancer can be compared with each other after calculation, and then the probabilistic sensitivity analysis can be performed. All model parameters have random values based on their individual distribution. The uncertainty of the model results is comprehensively estimated by randomly sampling all input parameters of the model at the same time(van Nuland et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe beta-distribution is used to express the uncertainty of utility, probability, and proportion, and the gamma-distribution is used for cost data(see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the results of PSA, we plotted a cost-effectiveness acceptability curve to show the proportion of different levels of cost- effectiveness t simulation. In addition, we performed a one-way sensitivity analyses to examine the influence of relevant variables on the results of the cost-effectiveness analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBase‑case treatment analyses\u003c/h2\u003e\n \u003cp\u003eIn our model, we calculated the lifetime cumulative costs, quality adjusted life years(QALYs), incremental QALYs, incremental costs, incremental cost-effectiveness ratios(ICERs), and incidences of death for the five treatment strategies(Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The Markov model calculated that the four distribution proportions of the VEX program were stabilization (0.1%), remission (16.8%), relapse (73.1%) and death (10%). Additionally, the four distribution proportions of the P program were stabilization (0.3%), remission (18.8%), relapse (69.7%) and death (11.2%)(see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Markov model predicted that the total costs of a 5-year horizon incurred with the VEX and P regimen were \u003cspan\u003e$\u003c/span\u003e74617.32 and \u003cspan\u003e$\u003c/span\u003e6020.53, respectively. Moreover, VEX gained an additional 0.4 QALYs compared with P. As a result, the ICER of VEX is \u003cspan\u003e$\u003c/span\u003e40 333.69/QALY, which is higher than the WTP threshold of \u003cspan\u003e$\u003c/span\u003e37 653.0/QALY. The ICER of P is \u003cspan\u003e$\u003c/span\u003e4 152.09/QALY, which is less than the WTP threshold of \u003cspan\u003e$\u003c/span\u003e37 653.0/QALY. This indicates that P regimen is cost-effective when compared with VEX regimen. The results of the base case analysis are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The economic outcomes of alternative strategies are presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The VEX strategy is the dominant strategy.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCost and effect of ACTH and TCH within five years\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVEX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeviation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Free Survival/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCosts (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74617.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6020.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68596.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICER(\u003cspan\u003e$\u003c/span\u003e/QALY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40333.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4152.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171491.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneral safety\u003c/h2\u003e\n \u003cp\u003eThe most common AEs (any grade) during the neoadjuvant period were anemia, infection, anorexia, diarrhea, constipation, nausea, fatigue and neutrophil count decreased (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The most common grade 3\u0026ndash;4 AEs were fatigue, anemia and neutrophil count decreased (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Fatigue was reported in 5 patients (7.1%) in the VEX group and 1 patient (1.6%) in the P group. The incidence of peripheral sensory neuropathy was also higher in P Group, with 5 patients (7.9%). The most common serious AEs were neutral count decreased. 20 patients (28.6%) in the VEX group had neutral count decreased, while 9 patients (14.3%) in the P group had neutral count decreased. No grade 5 AEs were reported(Insinga et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu et al.\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdverse events (G1-2 and G3-4) for VEX vs P\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVEX Adverse events (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP Adverse events (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG1-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG3-4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG1-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG3-4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlopecia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAllergic reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnorexia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstipation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNausea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeripheral sensory neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil count decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analyses\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eSingle‑factor sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eThis study calculated the corresponding transmission probability based on relevant literature data, and applied the maximum and minimum values in the literature data to calculate the corresponding transmission probability values as the scope of sensitivity analysis. The drug prices, incidence of adverse reactions, and cost of adverse reactions in this study are all sourced from real data, while health utility data is sourced from literature. Therefore, sensitivity analysis method is adopted to perform single factor sensitivity analysis on all parameters of the input Markov model. The results were summarized in a tornado diagram (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIt is not difficult to see from the graph that every transition probability that changes within its sensitivity range will more or less affect the final conclusion. Therefore, transition probabilities are important variables that affect the conclusion of the model. The six parameters that have the greatest impact are: the relapse transition probability of P regimen (PPpd), the transition probability of P regimen from remission to remission (Prr), the stable transition probability of P regimen (PPsd), the transition probability of P regimen from stable to stable (Pss), the transition probability of P regimen from stabilization to remission (Psr), and the transition probability of P regimen from relapse to death (Ppd). Nevertheless, other parameters, including drug costs and other disease utilities, had little impact on the robustness of the model.\u003c/p\u003e\n \u003cp\u003eAs the health utility value in this study only comes from foreign literature, this parameter was unstable. However, after sensitivity analysis of each parameter, it was found that the transfer probability of relapse had the greatest impact on the model results among the two chemotherapy regimens.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eResults of probability sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eProbability sensitivity analysis adopts Monte Carlo simulation method. Each simulation will generate a Markov queue of 10000 people. The total number of Markov queues is 1000. After calculation, the ICERs of neoadjuvant chemotherapy for two kinds of breast cancer are compared.\u003c/p\u003e\n \u003cp\u003eIn addition, based on the Monte Carlo simulation results, we compared the scatter plots of the probability sensitivity analysis between the VEX regimen and the P regimen. Among them, the horizontal and vertical axes represent the incremental effect and incremental cost of the VEX regimen relative to the P regimen, respectively. Each scatter in the figure represents the VEX regimen. The incremental cost-effectiveness ratio (ICER) is shown in the figure. Most of the scattered points in the figure are distributed in the first quadrant, and ICER ratio is generally positive. This means that the ICERs of 10000 Monte Carlo simulations are all above the WTP threshold, indicating that the VEX regimen does not have a cost-effectiveness advantage compared to the P regimen (see Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor a cohort of 1000 breast cancer patients, the cost-effectiveness acceptable curve can be derived from the mutual comparison of the two regimens. It can be seen from Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e that within the WTP range, the probability of the VEX regimen has a cost effect which is close to 0, and the P regimen acceptance curve is maintained at a high level, which indicates that the P regimen has the highest acceptable probability and is the preferred solution(see Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present, chemotherapy is still an important method for clinical treatment of patients with metastatic breast cancer, but most of the traditional chemotherapy programs use \"maximum tolerated dose (MTD)\" to treat patients. Although MTD has a good killing effect on tumor cells, due to its high toxicity, many patients have adverse reactions, and some patients even terminate treatment because they can\u0026rsquo;t tolerate toxic side effects. Moreover, in traditional chemotherapy, there is a long interval between two chemotherapy cycles, which can easily cause tumor cells to regrow, disease progression, and even lead to tumor cell resistance(Kerbel \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In recent years, the metronomic chemotherapy model that uses low-dose, low toxicity, and continuous application of anti-tumor drugs to inhibit tumor angiogenesis and promote tumor cell apoptosis has received much attention(Di Desidero et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Metronomic chemotherapy has better anti angiogenic effects and does not increase toxic side effects(Cazzaniga et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Taking Vinorelbine as an example, previous basic research has shown that compared to traditional chemotherapy, Vinorelbine metronomic chemotherapy can significantly downregulate the expression of pro angiogenic genes and upregulate the expression of antiangiogenic genes(Biziota et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, the study by Mavroeidis L et al. confirmed that the blood drug concentration of Vinorelbine metronomic chemotherapy can effectively inhibit the proliferation of vascular endothelial cells(Mavroeidis et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Vitro experiments have shown that metronomic chemotherapy combined with antiangiogenic drugs can effectively reduce tumor stem cell generation(Vives et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Folkins et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe METEORA-II trial showed that compared with weekly intravenous paclitaxel chemotherapy, oral vinorelbine\u0026thinsp;+\u0026thinsp;cyclophosphamide\u0026thinsp;+\u0026thinsp;capecitabine metronomic chemotherapy significantly prolonged TTF and PFS, and had a higher disease control rate(Munzone et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The OS of the two groups was similar. Although the toxic reactions of VEX increased, they were basically controllable. However, in the adjuvant treatment of metastatic breast cancer, patients bear a great financial burden. The economic evaluation of postoperative adjuvant therapy is crucial for maintaining a balance between clinical benefits and medical costs, especially in developing countries such as resource- limited China(Huang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, we have established a cost- effectiveness analysis of vinorelbine\u0026thinsp;+\u0026thinsp;cyclophosphamide\u0026thinsp;+\u0026thinsp;capecitabine (VEX regimen) and weekly intravenous paclitaxel (P regimen) for the treatment of metastatic breast cancer patients in China. This is the first time to analyze ER+/HER2 adjuvant treatment strategies for metastatic breast cancer from the perspective of efficacy and cost-effectiveness.\u003c/p\u003e \u003cp\u003eDifferent countries use different WTP thresholds for medical cost-effectiveness analysis. The cost effectiveness threshold range recommended by the United States is \u003cspan\u003e$\u003c/span\u003e50000 to \u003cspan\u003e$\u003c/span\u003e200000 per QALY(Neumann, Cohen, and Weinstein \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although China has not set a cost-effectiveness threshold, the Chinese Pharmacoeconomic Evaluation Guidelines suggest that if the ICER is below the threshold of per capita GDP (approximately \u003cspan\u003e$\u003c/span\u003e34240 in 2015), the additional cost of new therapies is worthwhile; If the ICER is between three times the per capita GDP and per capita GDP, the additional cost of the new therapy is acceptable; If the ICER is greater than three times the per capita GDP, then the additional cost of this new therapy is not worth it. In China, the most widely used cost-effectiveness threshold when evaluating new therapies is three times the per capita GDP(Liao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In our analysis, the ICER of the VEX regimen is \u003cspan\u003e$\u003c/span\u003e40 333.69/QALY, which is higher than per capita GDP but much lower than three times per capita GDP. Therefore, according to the Chinese Pharmacoeconomic Guidelines, VEX is acceptable as a treatment for advanced breast cancer. The ICER of P regimen is \u003cspan\u003e$\u003c/span\u003e4 152.09/QALY, which is lower than the per capita GDP. Although the VEX regimen improves patient TTF and PFS, it requires significant additional costs. Even with additional sensitivity analysis indicating that the VEX regimen improves AEs management, we cannot determine whether the high cost of the VEX regimen is reasonable. Therefore, in China, the P regimen may be a more cost-effective option.\u003c/p\u003e \u003cp\u003eThe high prices of anti-cancer drugs have led to a sharp increase in the consumption of medical resources, which has troubled clinical doctors and medical managers. The healthcare expenditures of high-income countries such as the United States and Europe have been increasing year by year, and they have explored potential methods for pricing cancer treatment drugs, hoping to maintain a sustainable impact on healthcare systems(Godman et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Low and middle-income countries are also actively addressing the issue of rising cancer treatment costs. Especially with the diversification of cancer treatment methods, choosing new anti-tumor drugs such as Vinorelbine may not be cost-effective in low and middle-income countries(Gershon et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, it is of great significance to evaluate treatment plans economically and make reasonable and effective use of limited medical resources. However, to our knowledge, China's medical insurance has already covered as many people as possible. Although there are significant differences in the reimbursement ratios of different regions and types of medical insurance, choosing a treatment plan with better results and lower costs is beneficial for the rational use of medical insurance.\u003c/p\u003e \u003cp\u003eTherefore, we believe that properly increasing the reimbursement proportion of VEX regimen in patients with metastatic breast cancer will help to save medical expenses and benefit more patients. In general, from the perspective of Chinese payers, P regimen is a cost-effective strategy for first-line systemic treatment of metastatic breast cancer.\u003c/p\u003e \u003cp\u003eDue to data availability and model assumptions, our economic model has some limitations. Firstly, the clinical data comes from the METEORA-II trial(Munzone et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where the majority of patients were from Italy and the proportion of Asians was small and unavailable, which may have a slight impact on our results. Due to the lack of research results on the health effects of breast cancer in different states in China, this limitation cannot be avoided at present. Updating the clinical data of metastatic breast cancer patients in the Chinese population may improve the accuracy in the future. Secondly, in this study, we assume that the transition probability of the Markov model remains unchanged during the study period. However, in the actual processing process, the transition probability between different states varies over time. Thirdly, the incidence of adverse reactions in different disease states of breast cancer is different, and the incidence of adverse reactions generally changes with time. The clinical real data relied on by this study only provides the incidence of adverse reactions for the entire treatment group, and does not provide the incidence of adverse reactions for different disease states. Our study also did not discuss the decrease in utility value caused by adverse effects. However, sensitivity analysis indicates that changes in utility values do not alter the results in terms of quality. Fourthly, in our analysis, we did not consider indirect costs such as income loss caused by discontinuation of treatment and premature death, as the high variability of the condition makes it difficult to accurately calculate. Fifthly, drug prices and treatment costs are derived from previously published literature or local data and may not be applicable to all regions. To avoid the impact of these costs on the model, we changed the treatment costs within a considerable range (\u0026plusmn;\u0026thinsp;20%) in the one-way sensitivity analysis. Variation is mediated by these factors and is limited. Although our research has certain limitations, after sensitivity analysis, these variables do not affect the final results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to traditional economic evaluation methods, the chemotherapy cost of P regimen is lower than that of VEX regimen. Although the TTF and PFS of the VEX regimen are higher than those of the P regimen in terms of efficacy, and the disease control rate of the VEX regimen is also higher than that of the P regimen, this requires a significant additional cost, and the OS of the two regimens is ultimately similar. In addition, the analysis of incremental cost-effectiveness shows that with VEX as the reference group, P regimen. is the preferred option. Therefore, considering both cost and effectiveness, compared to the VEX regimen, the P regimen is a cost-effective option, and at the current price, the P regimen may benefit more ER+/HER2- metastatic breast cancer women in China. We hope that our research can provide references for doctors or patients when making treatment decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the teachers of Affiliated Hangzhou First People\u0026rsquo;s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNR and QPX were major contributors to writing the manuscript. LQR drew the schematic diagram, JJP and YBY completed the literature search.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Key Medical Discipline of Hangzhou City (2021\u0026ndash;21); Key Medical Discipline of Zhejiang Province (2018\u0026ndash;2\u0026ndash;3); Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province (2020E10021); Zhejiang Province Medical and Health Science and Technology Program (2023KY933); Zhejiang Traditional Chinese Medicine Science and Technology Project (2023ZL565).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are in agreement with the content for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e《中国药物经济学评价指南》课题组 (2011) 刘国恩, 胡善联, and 吴久鸿. 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Breast Cancer Res Treat 177:549\u0026ndash;559\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ER+/HER2- metastatic breast cancer, metronomic chemotherapy, Pharmaceutical economics, Markov model, Cost-effectiveness analysis","lastPublishedDoi":"10.21203/rs.3.rs-3860294/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3860294/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e：To compare the cost-effectiveness of Metronomic Oral Vinorelbine plus Cyclophosphamide and Capecitabine(VEX) and Weekly Intravenous Paclitaxel (P) in patients with Estrogen Receptor–Positive, ERBB2-Negative Metastatic Breast Cancer (MBC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e：The Markov model was established to simulate the patients receiving metronomic chemotherapy (VEX regimen) and Weekly Intravenous Paclitaxel. The results of clinical trials and other published literature were comprehensively used to evaluate the cost-effectiveness ratio of the two chemotherapy regimens, and sensitivity analyses were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e：The QALYs of VEX and P regimen were 1.85 and 1.45, respectively, and the ICERs were $40 333.69/QALY and $4 152.09/QALY, respectively. In China, the total cost of VEX regimen is $74 617.32, while the total cost of P regimen is $6 020.53. The cost of P regimen is much lower than that of the VEX regimen. In addition, the VEX is more effective than the P, with higher TTF and PFS, and higher disease control rates. Sensitivity analysis shows that P regimen has a more cost-effective advantage in China. The analysis of incremental cost-effectiveness shows that with VEX as the reference group, P regimen is the preferred option.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e：Compared with VEX, P regimen is more cost-effective as a first-line treatment for ER+/HER2- metastatic breast cancer from the perspective of Chinese health service system.\u003c/p\u003e","manuscriptTitle":"Cost-effectiveness of Metronomic chemotherapy vs Weekly Intravenous Paclitaxel in patients with ER+/HER2-Metastatic Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 19:02:08","doi":"10.21203/rs.3.rs-3860294/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15229bf4-a9d2-47d1-ac8a-7638980fb437","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-30T14:20:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 19:02:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3860294","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3860294","identity":"rs-3860294","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}