Comparison of Efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy for patients with advanced non-small cell lung cancer: a real-world study

Comparison of Efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy for patients with advanced non-small cell lung cancer: a real-world study | 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 Comparison of Efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy for patients with advanced non-small cell lung cancer: a real-world study Ning Wan, Chen Yang, Bing Wang, Ya Guo, ZiJian He, YaJuan Lv, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4254848/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 Background: While pembrolizumab has demonstrated effectiveness in clinical trials for non-small cell lung cancer (NSCLC), its real-world efficacy and cost-effectiveness, particularly considering its high cost, remain uncertain. This study aimed to compare the clinical efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy in treating patients with advanced NSCLC. Methods: In this retrospective cohort study, advanced NSCLC patients treated with pembrolizumab (either as monotherapy or combined with chemotherapy) and chemotherapy alone were analyzed from April 2017 to March 2023 at a major 3A Hospital. Primary outcomes included progression-free survival (PFS), overall survival (OS), and the incremental cost-effectiveness ratio (ICER). Secondary outcomes were the objective response rate (ORR), disease control rate (DCR), and adverse events (AE). Results: The study involved 630 patients, with 169 in the pembrolizumab group and 461 in the chemotherapy group. Post propensity score matching (PSM), the sample size was 450 (149 in pembrolizumab, 301 in chemotherapy). Pembrolizumab showed a significantly higher ORR (48.63% vs. 36.00%, p0.05) compared to chemotherapy. The median PFS was longer with pembrolizumab (15.5 months vs. 8.8 months, p<0.001), and the median OS was not reached compared to 26.2 months in chemotherapy. In second-line treatments, pembrolizumab showed superior PFS and OS. From the perspective of the Chinese healthcare system, pembrolizumab was not cost-effective compared to chemotherapy at a willingness-to-pay threshold of $36,070.2/QALY but was cost-effective at three times the per capita GDP in Guangzhou. Conclusion: Pembrolizumab demonstrates superior clinical efficacy over chemotherapy in a real-world setting for advanced NSCLC, with manageable AEs. Its cost-effectiveness varies by regional economic conditions and payment thresholds, suggesting potential economic feasibility in economically developed areas with drug grant policies. pembrolizumab non-small cell lung cancer real-world cost-effectiveness chemotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 | INTRODUCTION Lung cancer remains the leading cause of cancer deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all lung cancer types. 1, 2 Conventional platinum-containing double combination chemotherapy is the standard first-line treatment for advanced NSCLC; however, its survival rate is low. 3, 4 In the past few years, significant advancements in lung cancer treatment have been achieved through the development of immune checkpoint inhibitors (ICIs), specifically targeting programmed death receptor 1 (PD-1) and its ligand PD-L1. These ICIs work by impeding the interaction between PD-1 and PD-L1, effectively hindering the proliferation of cancer cells. Pembrolizumab, one of the ICI drugs, is proven to be clinically effective for advanced NSCLC in multiple randomized controlled trials (RCTs) and has become one of the clinical treatment options. Drawing on the results of the KEYNOTE-024 and KEYNOTE-010 studies, 5, 6 it was observed that pembrolizumab used alone significantly benefitted patients with advanced NSCLC who had not undergone previous treatments and had a PD-L1 tumor proportion score (TPS) of 50% or more. Following the outcomes of the KEYNOTE-024 clinical trial, pembrolizumab was established as the standard treatment for patients with NSCLC who have a PD-L1 TPS of 50% or higher. Moreover, in the KEYNOTE-189 and KEYNOTE-047 trials, 7, 8 the effectiveness of combining pembrolizumab with chemotherapy exceeded that of chemotherapy alone in treating patients with metastatic non-squamous and squamous NSCLC, demonstrating a higher degree of clinical benefit. Despite the exciting results of this new immunotherapy. However, the high prices associated with these new therapies pose a significant challenge to the healthcare system. Relevant economic studies have shown that the current price tag of pembrolizumab is not cost-effective and that it needs to be reduced to be cost-effective compared to commonly used chemotherapy treatments. 9–11 However, all of the above studies are economic analyses based on the results of RCTs, and there is a noticeable scarcity of economic analyses based on outcomes from real-world studies. While RCTs have shown the clinical benefits of using pembrolizumab alone or alongside chemotherapy in the management of advanced NSCLC, there may be some differences between the patients enrolled in RCTs and those in real-world situations due to its strict criteria. To date, there have been several real-world studies exploring the efficacy and safety of pembrolizumab, but few have been reported economically. Therefore, it is also important to understand efficacy, safety, and economics in real-world clinical settings. Good observational studies can also provide some degree of evidence to complement clinical trials. This study aimed to assess the effectiveness, safety, and cost-effectiveness of pembrolizumab, both as a single-agent therapy and in combination with chemotherapy, compared to chemotherapy alone in treating advanced NSCLC in real-world settings. 2 | MATERIALS AND METHODS 2.1 | Study design and patients This research involved a retrospective cohort analysis of advanced NSCLC patients treated with either pembrolizumab monotherapy or in combination with chemotherapy and chemotherapy at large 3A Hospital. The inclusion criteria were ( 1 ) 18 years or older, ( 2 ) Participants required a first-time diagnosis of stage III/IV NSCLC by imaging and pathological tissue, ( 3 ) At least 1 measurable lung lesion, ( 4 ) At least 2 weeks of treatment with pembrolizumab or conventional chemotherapy, ( 5 ) Routine baseline examinations must be performed during or before treatment with pembrolizumab or conventional chemotherapeutic agents: chest and abdomen scan + enhanced computed tomography (Computer Tomography, CT), complete biochemistry, routine blood work, cardiac enzymes, plasma adrenocorticotropic hormone, serum cortisol, two pancreatic enzymes and so on. ( 6 ) Patients receiving pembrolizumab monotherapy or in combination with chemotherapy and platinum-based chemotherapy. The exclusion criteria were ( 1 ) Individuals diagnosed with additional malignant tumors, ( 2 ) Participants who have an expected survival of less than 1 month, ( 3 ) Patient adherence to treatment with poor compliance and incomplete medical records, ( 4 ) Patients in the pembrolizumab group received multiple ICIs, such as the using of nivolumab, atezolizumab and so on, ( 5 ) Patients are treated with pembrolizumab along with other targeted agents. For cost-effectiveness analysis, a partitioned survival model was employed to forecast the anticipated expenses and results of pembrolizumab relative to chemotherapy. The model was developed as a partitioned survival model, comprising three distinct health states: progression-free (initial state), progressive disease, and death. The model cycling period in the study was set at three weeks consistent with the dosing cycle, and the model predicted time levels for the entire patient lifespan. In this research, both cost and utility data were subjected to a 5% discount rate for calculation purposes. The willingness-to-pay threshold for the Incremental Cost-Effectiveness Ratio (ICER) was established at three times the 2022 Gross Domestic Product (GDP) per capita in China, which was $ 36070.2. Given that the real-world study took place in Guangzhou, the 2022 per capita GDP of Guangzhou was similarly utilized as the willingness-to-pay threshold for the Incremental Cost-Effectiveness Ratio (ICER), amounting to $ 64523.8. 2.2 | Outcomes Tumor response was evaluated based on the RECIST 1.1 criteria, 12 which include categories such as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). The primary outcomes measured in this study were progression-free survival (PFS), overall survival (OS), and the Incremental Cost-Effectiveness Ratio (ICER). PFS was computed from the initiation of treatment until the occurrence of disease progression (as evaluated by RECIST 1.1 criteria), death from any cause, or the latest follow-up date (March 31, 2023). OS was calculated from the commencement of pembrolizumab treatment until the event of death from any cause or the most recent follow-up. ICER stands for the cost per quality-adjusted life year (QALY) gained. The secondary endpoints encompassed the objective response rate (ORR), which measures the percentage of patients achieving CR or PR as the best response, the disease control rate (DCR), which quantifies the percentage of patients with CR, PR, or SD as the best response, and the assessment of adverse events (AEs). AEs were evaluated by the National Cancer Research Institute Common Terminology Criteria for Adverse Events version 5.0. Tumor response was determined for eligible patients who underwent a minimum of 2 treatment cycles for tumor assessment. AEs were evaluated for all patients who completed at least one treatment cycle. 2.3 | Data collection The study data were extracted from the Hospital His system, including (i) Data from patient admissions: name, age, gender, height, weight, BMI, and so on; (ii) Data from electronic medical records: smoking history, complaints, diagnosis, medication use, adverse reactions, and so on; (iii) Data from prescription information: drug type, dosage, period of validity, number of lines of treatment, and so on. The collected data was augmented and structured by incorporating the patient's prior treatment history, the Eastern Cooperative Oncology Group (ECOG) performance status (PS) score, pathological histological type, TNM staging, routine blood, complete biochemistry, tumor markers, imaging tests, personal disease history (presence of hypertension, diabetes mellitus, tuberculosis (hypertension, diabetes, tuberculosis, dyslipidemia, and so on) and other relevant information. Additionally, a propensity score matching analysis was performed by matching for gender, age, BMI, ECOG score, pathological histological type, combination therapy status, TNM stage, and the count of treatment lines for patients in both groups considered. In terms of economic evaluation, the research was designed from the standpoint of the Chinese healthcare system, focusing solely on the analysis of direct medical expenses incurred by patients. Direct medical expenses encompass the following components: the expenses related to treatment drugs, examination costs, ancillary treatment costs, regular follow-up costs, the cost of subsequent drug treatment following disease progression, and expenditures associated with adverse drug events. The drug costs were obtained from the relevant drug prices published by the GPO platform of Guangdong Province, and the costs of supporting treatment, adverse drug, and relevant examinations were obtained from published literature. The treatment protocols administered to patients from the onset of treatment until the initial occurrence of disease progression, as well as the subsequent treatment protocols following disease progression, were derived from real-world data. The patient treatment regimens were then compiled and weighted to ascertain the contribution of each treatment regimen to the overall regimen within each treatment group; for patients who appeared disease progression but were not clear about the subsequent treatment regimen, the expenses associated with their subsequent treatment protocol defaulted to the cost of supportive treatment, and for patients who died while patients who developed disease progression, the cost of their follow-up regimen defaulted to $ 0. In addition, the study incorporated the expenses related to grade 3 ~ 5 adverse events that occurred in both groups and weighted them based on the observed incidence. 2.4 | Statistical analysis Statistical analysis was conducted using SPSS software, specifically version 25.0, while the graphs were generated using GraphPad Prism, with version 9.0 employed for this purpose. Descriptive statistics were utilized to provide an overview of the patient's clinical characteristics. Continuous variables were presented using median and range, while categorical variables were represented through counts and percentages. Comparisons of baseline clinical characteristics between groups were conducted using the t-test. The chi-square test and Fisher's exact probability method (Monte Carlo, MC) for count data. A P-value less than 0.05 was deemed to indicate statistical significance. To reduce confounding bias between the pembrolizumab and chemotherapy groups, propensity score matching (PSM) was employed. PFS and OS were evaluated using the Kaplan-Meier method, and comparisons were made with the log-rank test. We conducted univariate and multivariate Cox proportional hazards regression analyses to identify potential prognostic biomarkers, examining the relationship between clinicopathological characteristics and both PFS and OS. Variables that demonstrated a p-value below 0.10 in univariate analysis were incorporated into the multivariate analysis. The results were presented as hazard ratios (HRs) with 95% confidence intervals (CIs). In the cost-effectiveness analysis, we extracted data from the Kaplan-Meier survival curves of PFS and OS using GetData Graph Digitizer software. Subsequently, these PFS and OS curves were fitted and extrapolated using R software (version 4.2.3) along with the SURVHE package. The optimal curve fit was determined based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), complemented by visual inspection. This study employed Deterministic Sensitivity Analysis (DSA) to evaluate the impact of variations in parameters such as drug pricing, utility values of no-progress and progress states, and discount rate within the model, ensuring the stability of the results. Additionally, Probabilistic Sensitivity Analyses (PSA) were conducted. During the DSA, parameter values underwent a ± 20% variation from their original values. For the Probabilistic Sensitivity Analysis (PSA), 1000 Monte Carlo simulations were executed, employing repeated sampling in line with the variables' range and distribution. Here, the Gamma distribution was applied to cost data, and the Beta distribution was utilized for utility values. The outcomes of these Monte Carlo simulations were then used to construct cost-effectiveness acceptability curves. 3 | RESULTS 3.1 | Characteristics of the patients 630 patients with advanced NSCLC were identified through an electronic database as having received pembrolizumab (monotherapy or combination chemotherapy) and chemotherapy. Among these individuals, 169 patients received pembrolizumab treatment, either alone or in combination with chemotherapy, while 461 patients underwent chemotherapy. The baseline demographic, clinical, and pathologic characteristics of the 630 patients were displayed in Table 1 . The gender differences, TNM stage, pathological histology, the number of treatment lines (first/second-line treatment), and smoking status between the two groups before PSM were statistically significant (p 0.05). Table 1 Baseline characteristics and treatment details of NSCLC patients receiving pembrolizumab or chemotherapy. Variables Before PSM (n = 630) After PSM (n = 450) Patients treated with pembrolizumab group (n = 169) Patients treated with chemotherapy group (n = 461) p-value Patients treated with pembrolizumab group (n = 149) Patients treated with chemotherapy group (n = 301) p-value Age, years- Median (Interquartile) 61.0 (53.0–67.0) 60.0 (53.0–66.0) 0.325 60.0 (52.0–67.0) 61.0 (55.0-66.5) 0.492 BMI, Median (Interquartile) 22.8 (20.6–25.2) 23.1 (20.8–25.1) 0.703 22.8 (21.2–25.2) 22.9 (20.8–25.1) 0.345 Sex, n (%) < 0.050 1.000 Male 134 (79.3%) 316 (68.5%) 117 (78.5%) 228 (75.7%) Female 35 (20.7%) 145 (31.5%) 32 (21.5%) 73 (24.3%) Histology subtype, n (%) < 0.050 0.625 Squamous 52 (30.8%) 60 (13.0%) 39 (26.2%) 53 (17.6%) Non-Squamous 116 (68.6%) 396 (85.9%) 110 (73.8%) 248 (82.4%) NA 1 (0.6%) 5 (1.1%) TNM stage, n (%) < 0.050 0.319 Ⅳ 125 (74.0%) 330 (71.6%) 112 (75.2%) 250 (83.1%) Ⅲ 29 (17.1%) 109 (23.6%) 24 (16.1%) 35 (11.6%) NA 15 (8.9%) 22 (4.8%) 13 (8.7%) 16 (5.3%) ECOG PS at diagnosis, n (%) 0.817 0.455 0–1 141 (83.4%) 394 (85.5%) 125 (83.9%) 257 (85.4%) ≥ 2 6 (3.6%) 14 (3.0%) 6 (4.0%) 7 (2.3%) NA 22 (13.0%) 53 (11.5%) 18 (12.1%) 37 (12.3%) Smoking status (%) < 0.050 0.115 Previous Smoking 89 (52.7%) 186 (40.3%) 80 (53.7%) 153 (50.8%) Never smoked 62 (36.7%) 252 (54.7%) 61 (40.9%) 129 (42.9%) NA 18 (10.6%) 23 (5.0%) 8 (5.4%) 19 (6.3%) The number of treatment lines (%) < 0.050 0.096 1 112 (66.3%) 344 (74.6%) 96 (64.4%) 203 (67.4%) ≥ 2 57 (33.7%) 117 (25.4%) 53 (35.6%) 98 (32.6%) 3.2 | Tumor response After PSM, a total of 149 patients were enrolled in the pembrolizumab group, 3 of whom had the best overall outcome of NE, and 301 patients were enrolled in the chemotherapy group, 1 of whom had the best overall outcome of NE. Within the pembrolizumab group, 71 patients experienced PR, and 68 had SD, resulting in an ORR of 48.63% and a DCR of 95.21%. In the chemotherapy group, there was 1 case of CR, 107 of PR, and 161 of SD, leading to an ORR of 36.00% and a DCR of 90.00%. There was a significant difference in ORR between the two groups (p = 0.011). Detailed information about the effectiveness of each treatment regimen is presented in Table 2 . In addition, we performed an overall efficacy analysis for patients treated in the first line. After excluding NE patients, 95 patients were enrolled in the pembrolizumab group and 205 patients were enrolled in the chemotherapy group. Consequently, after matching, the ORR for first-line treatment was 58.95% in the pembrolizumab group and 36.63% in the chemotherapy group (p = 0.001), while the DCR was 97.89% and 91.09% (p = 0.029), respectively. The pembrolizumab group demonstrated higher ORR and DCR compared to the chemotherapy group. Detailed data on the efficacy of each treatment regimen can be found in Table 3 . In the population receiving second or subsequent lines of treatment, after excluding patients with NE, 51 patients were included in the pembrolizumab group and 98 in the chemotherapy group. The analysis indicated that the differences in ORR and DCR between the pembrolizumab and chemotherapy groups were not statistically significant (p > 0.05). This data is detailed in Table 4 . Table 2 Best overall efficacy after matching in the overall population. Treatment response, n (%) Pembrolizumab group (n = 146) Chemotherapy group (n = 300) p-value Complete response 0 (0) 1 (0) Partial response 71 (48.63) 107 (35.67) Stable disease 68 (46.57) 161 (53.67) Progressive disease 7 (4.80) 31 (10.33) Objective response rate 71 (48.63) 108 (36.00) 0.011 Disease control rate 139 (95.21) 270 (90.00) 0.061 Table 3 Best overall efficacy after matching in the first-line treatment population. Treatment response, n (%) Pembrolizumab group (n = 95) Chemotherapy group (n = 202) p-value Complete response 0 (0) 1 (0) Partial response 55 (57.89) 73 (36.14) Stable disease 38 (40.00) 110 (54.45) Progressive disease 2 (2.10) 18 (8.91) Objective response rate 56 (58.95) 74 (36.63) 0.001 Disease control rate 93 (97.89) 184 (91.09) 0.029 Table 4 Best overall efficacy after matching in the second and more lines treatment population. Treatment response, n (%) Pembrolizumab group (n = 51) Chemotherapy group (n = 98) p-value Complete response 0 (0) 0 (0) Partial response 16 (31.37) 34 (34.69) Stable disease 30 (58.82) 51 (52.04) Progressive disease 5 (9.80) 13 (13.26) Objective response rate 16 (31.37) 34 (34.69) 0.684 Disease control rate 46 (90.20) 85 (86.73) 0.538 3.3 | Survival analysis At the final evaluation point, out of the total patients, 281 (63.00%) experienced disease progression. Specifically, in the pembrolizumab group, 91 patients (62.33%) had disease progression, while in the chemotherapy group, this number was 190 (63.30%). The median PFS was notably different between the two groups: patients in the pembrolizumab group had a median PFS of 15.5 months (95%CI: 11.8–19.2), in contrast to the chemotherapy group, which had a median PFS of 8.8 months (95%CI: 7.5–10.1). This difference was statistically significant, with a Hazard Ratio (HR) of 0.611 (95% CI, 0.483–0.774; p < 0.001), as illustrated in Fig. 1A. For patients in the pembrolizumab group, the median OS had not been reached at the time of analysis. In contrast, for those in the chemotherapy group, the median OS was recorded at 26.2 months (95%CI: 0.483–0.774). The difference in survival outcomes between the two groups was significant, with an HR of 0.532 (95%CI, 0.399–0.709; p < 0.001), as depicted in Fig. 2E. Additionally, Supplementary Tables S1 and S2 present both univariable and multivariable analyses of factors influencing PFS and OS. However, these analyses revealed no factors significantly associated with PFS and OS, as all p-values were greater than 0.05. In first-line treatment, patients in the pembrolizumab group had a median PFS of 15.5 months (95%CI: 12.4–18.6), compared to 9.6 months (95%CI: 7.5–11.7) for those in the chemotherapy group. This resulted in an HR of 0.615 (95% CI, 0.458–0.824; p = 0.002), as shown in Fig. 1B. Regarding median OS, it had not been reached for patients in the pembrolizumab group, whereas it was 27.7 months (95% CI: 20.2–35.2) for the chemotherapy group. The statistical significance of this difference in survival rates was underscored by an HR of 0.538 (95% CI, 0.378–0.765; p = 0.002), as illustrated in Fig. 2F. In second or more lines of treatment, the median PFS for patients receiving pembrolizumab was 11.7 months (95%CI: 4.7–18.7), while for those undergoing chemotherapy, it was 7.7 months (95%CI: 7.0-8.4). This difference was statistically significant, with an HR of 0.600 (95%CI, 0.404–0.893; p = 0.013), as displayed in Fig. 3 A. As for the median OS, it had not been reached in the pembrolizumab group, in contrast to 25.6 months (95%CI: 22.1–29.1) in the chemotherapy group. This outcome also demonstrated a significant difference, indicated by an HR of 0.025 (95%CI, 0.319–0.866; p = 0.025), which is illustrated in Fig. 3 B. In non-squamous patients, those treated with pembrolizumab experienced a median PFS of 17.4 months (95%CI: 15.0-19.8), compared to 8.8 months (95%CI: 7.0-10.6) for those in the chemotherapy group. This difference was significant, with an HR of 0.582 (95%CI: 0.446-0.760; p<0.001), as depicted in Figure 1C. Regarding median OS, it had not been reached for the pembrolizumab group, whereas it was 27.0 months (95%CI: 17.1-36.9) for the chemotherapy group. The difference in survival outcomes was noted with an HR of 0.476 (95%CI: 0.330-0.688; p=0.691), as shown in Figure 2G. For squamous patients, the median PFS was observed to be 10.0 months (95%CI: 4.2-15.8) in the pembrolizumab group and 8.0 months (95%CI: 5.1-10.9) in the chemotherapy group. The HR for this difference was 0.658 (95%CI: 0.392-1.108; p=0.100), as illustrated in Figure 1D. In terms of median OS, it was 30.0 months (95%CI: not reached [NR]) for the pembrolizumab group and 27.7 months (95%CI: 16.7-38.7) for the chemotherapy group, with an HR of 0.335 (95%CI: 0.367-1.440; p=0.335), as shown in Figure 2H. 3.4 | Adverse Events AEs of any grade were observed in 119 patients (79.87%) in the pembrolizumab group and 261 patients (86.71%) in the chemotherapy group, as detailed in Table 5 . AEs of grade 3 or higher were reported in 6 patients (4.03%) in the pembrolizumab group and 22 patients (7.31%) in the chemotherapy group. The most frequent AEs in the pembrolizumab group included anemia (52.35%), cough (20.80%), and leukopenia (19.46%). In contrast, the chemotherapy group primarily experienced anemia (69.77%) and leukopenia (20.60%). Table 5 The adverse drug reactions of the two groups. Pembrolizumab group Chemotherapy group Any Grade, n (%) Grade ≥ 3, n (%) Any Grade, n (%) Grade ≥ 3, n (%) Any AE 119 (79.87) 6 (4.03) 261 (86.71) 22 (7.31) Anemia 78 (52.35) 3 (2.01) 210 (69.77) 8 (2.66) Neutropenia 28 (18.79) 1 (0.67) 41 (13.62) 9 (2.99) Leukopenia 29 (19.46) 0 (0) 62 (20.60) 4 (1.33) Thrombocytopenia 14 (9.40) 2 (1.34) 28 (9.30) 2 (0.66) Creatinine elevation 12 (8.05) 0 (0) 25 (8.31) 0 (0) AST elevation 11 (7.38) 0 (0) 12 (3.99) 0 (0) ALT elevation 21 (4.09) 0 (0) 15 (4.98) 2 (0.66) Hypothyroidism 8 (5.37) 0 (0) 0 (0) 0 (0) Hyperthyroidism 1 (0.67) 0 (0) 0 (0) 0 (0) Rash 13 (8.72) 0 (0) 4 (1.33) 0 (0) Pruritus 10 (6.71) 0 (0) 4 (1.33) 0 (0) Decreased appetite 8 (5.40) 0 (0) 39 (12.96) 0 (0) Fatigue 24 (16.11) 0 (0) 29 (9.63) 0 (0) Cough 31 (20.80) 0 (0) 46 (15.28) 0 (0) Fever 9 (6.04) 1 (0.67) 12 (3.99) 0 (0) Constipation 7 (4.70) 0 (0) 12 (3.99) 0 (0) Headaches 9 (6.04) 0 (0) 12 (3.99) 1 (0.33) Vomiting 9 (6.04) 0 (0) 23 (7.64) 0 (0) Edema of the extremities 4 (2.68) 0 (0) 7 (2.33) 0 (0) Hengitysvaikeudet 4 (2.68) 0 (0) 15 (4.98) 1 (0.33) Nausea 12 (8.05) 0 (0) 41 (13.62) 1 (0.33) Diarrhea 5 (3.36) 0 (0) 11 (3.65) 0 (0) Pneumonitis 0 (0) 0 (0) 3 (1.00) 0 (0) Immune-related pneumonia 2 (1.34) 0 (0) 0 (0) 0 (0) Abbreviated terms: AE-adverse events; AST–aspartate aminotransferase; ALT–alanine aminotransferase. 3.5 | Cost-effectiveness analysis The K-M survival plots for PFS and OS in both the pembrolizumab and chemotherapy groups were derived from the dataset. These plots were tailored to individual patient data, and the corresponding fitted outcomes can be found in Additional file 1: Table S3. The best-fit distribution of the survival curves was judged based on the AIC value of each fitted distribution and the smaller the AIC value, the better the fit of the distribution. The best-fit distributions for the K-M survival curves of PFS and OS in both the pembrolizumab and chemotherapy groups were identified as log-normal distributions. The results of the fitted distribution parameters for each survival curve model are shown in Additional file 1: Table S4. The study encompassing 450 patients, which assessed the efficacy and safety of pembrolizumab and chemotherapy groups, compiled treatment regimens from the initiation of therapy to the first occurrence of PD. It also documented subsequent treatment strategies post-PD. The varieties of anticancer drugs utilized in these regimens are detailed in Additional file 1: Table S5. The cost of the relevant drugs was obtained from the Guangdong Provincial GPO platform. The expense for each cycle of various anticancer medications was determined using the treatment dosage guidelines for different drugs specified in the 2022 CSCO NSCLC treatment guidelines. This calculation was combined with the characteristics of a hypothetical patient, who has a Body Surface Area (BSA) of 1.80 m², weighs 65 kg, and has a Creatinine clearance rate (Ccr) of 90 ml/min/1.73 m². The upper and lower bounds of the drug costs were estimated by adjusting the drug cost by ± 20%. A uniform Gamma distribution was selected for the cost variability of drug treatments. Drawing from the findings of the aforementioned safety assessment, significant non-immune-related adverse reactions observed in both the pembrolizumab and chemotherapy groups include anemia, neutropenia, and thrombocytopenia. These will be factored into the cost calculation for adverse reactions, incorporating negative utility values for such reactions. The costs for prevention and treatment of adverse reactions, screening, and supportive care described above were obtained from published literature 13 and were shown in Additional file 1: Table S6. The study calculated utility values for patients without disease progression and those with disease progression, as well as negative utility values for three severe adverse reactions: anemia, neutropenia, and thrombocytopenia. Both the utility values for patients and the negative utility values for adverse reactions were sourced from literature 14 (Additional file 1: Table S7). All utility value data were subjected to ± 20% as upper and lower limits of utility values, and Beta distribution was chosen. Table 6 displays the outcomes of the cost-effectiveness analysis comparing the pembrolizumab group to the chemotherapy group, excluding the impact of any complimentary drug policy. For the chemotherapy group, the average cost per patient was $ 81784, with an average QALY of 2.63. In contrast, the pembrolizumab group experienced an enhanced clinical benefit of 1.69 QALY, but at an increased cost of $ 329021, resulting in an ICER of $ 146409.07/QALY when compared to the chemotherapy group. Table 6 presents the cost-effectiveness analysis results for the pembrolizumab group versus the chemotherapy group, considering the complementary drug policy. In this analysis, the average cost for the chemotherapy group was $ 59112 per patient, with a mean QALY of 2.63. The pembrolizumab group, on the other hand, showed a clinical benefit increase of 1.69 QALY but incurred a higher cost of $ 153893. This resulted in an ICER of $ 56127.74/QALY in comparison to the chemotherapy group. Table 6 Results of cost-utility analysis. Free medication is not considered Treatment COST QALYs C/E ΔCOST ΔQALYs ICER Chemotherapy 81784 2.63 91886.97 — — — Pembrolizumab 329021 4.32 187960.62 247237 1.69 146409.07 Consider medication giveaways Chemotherapy 59112 2.63 63789.80 — — — Pembrolizumab 153893 4.32 84001.05 94781 1.69 56127.74 3.6 | Sensitivity analysis The study conducted a DSA on several cost parameters including drug costs, utility values for two disease states, associated screening and treatment expenses, and utility values for adverse effects, with variations set at ± 20% of their base values. The WTP threshold was set at three times China's GDP per capita for the year 2022. The findings indicated that the price of pembrolizumab, the utility values associated with disease progression and non-progression, and the cost of CT scans significantly influenced the outcomes, both with and without the consideration of complementary drugs. These results are depicted in Fig. 4 and Fig. 5 . It was also noted that a reduction in the price of pembrolizumab would lead to a further decrease in the ICER values. This study conducted a PSA through 1000 Monte Carlo simulations, as shown in Fig. 6 and Fig. 7. From the cost-effectiveness acceptability curves, it's evident that when comparing the pembrolizumab group (without complimentary drugs) and the chemotherapy group, as well as the pembrolizumab group (with complimentary drugs) against the chemotherapy group, the chemotherapy group attains 100% acceptability at a cost threshold of approximately three times China's GDP per capita ( $ 36070.2), outperforming the pembrolizumab group. Additionally, in the pembrolizumab group with complimentary drugs, its cost-effectiveness acceptability curve relative to the chemotherapy group indicates that at a threshold of three times Guangzhou's per capita GDP ( $ 64523.8), pembrolizumab achieves 100% acceptability. 4 | DISCUSSION Although retrospective studies have previously confirmed the antitumor function of pembrolizumab in treating patients with advanced NSCLC, most of them were single-arm studies. 15–18 Fewer retrospective studies have directly compared and analyzed pembrolizumab with chemotherapy. Hence, this study was undertaken to further elucidate the effectiveness of pembrolizumab in a practical clinical environment. A real-world study revealed that the ORR for pembrolizumab combined with chemotherapy and for chemotherapy alone was 53.3% and 40.5%, respectively (p = 0.410). 19 However, this study had limitations, including a small sample size of only 54 patients (37 in the chemotherapy group and 17 in the pembrolizumab plus chemotherapy group), and a significant gender disparity in the patient cohort. In our current study, we conducted PSM analysis to address differences in patient demographics and assessed the effectiveness of both the pembrolizumab and chemotherapy groups. Following PSM, the study included 450 patients, with 149 in the pembrolizumab group and 301 in the chemotherapy group. The ORR was 48.63% for the pembrolizumab group and 36.00% for the chemotherapy group, showing statistical significance in our study (p = 0.011). Moreover, we conducted an additional analysis to compare the efficacy of first-line treatment versus second-line and subsequent treatments. The findings indicated that the ORR for first-line treatment with pembrolizumab and chemotherapy was 58.95% and 36.63%, respectively (p = 0.001), while for second-line and beyond, the ORR was 31.37% for pembrolizumab and 34.69% for chemotherapy (p = 0.684). These results suggest that pembrolizumab's effectiveness as a first-line treatment for patients with advanced NSCLC is more pronounced. In this research, a significant disparity in PFS was observed between the two groups in the general population, with an HR of 0.611 (95%CI, 0.483–0.774; p < 0.001). The median PFS for the pembrolizumab cohort was 15.5 months, surpassing the PFS outcomes reported in the clinical trial. 20 Furthermore, the median OS was not yet reached in the population treated with pembrolizumab. The overall population's chemotherapy group exhibited a median OS of 26.2 months. This extended OS duration in the chemotherapy patients, compared to earlier studies, could be attributed to the fact that several individuals in this group received second or subsequent lines of treatment that included a crossover with immune therapy agents like sintilizumab, camrelizumab, toripalimab, tislelizumab, or nivolumab. Previously, a retrospective study based on a Japanese population reported a median PFS of 18.4 months for first-line pembrolizumab monotherapy in NSCLC patients with PD-L1 ≥ 50%, and a median OS not achieved. 21 This is similar to the results of our study. Furthermore, our analysis extended to comparing the performance of the pembrolizumab and chemotherapy groups in populations receiving first-line and second or more lines of treatment. We found that in the first-line patient cohort, the pembrolizumab group exhibited superior outcomes in terms of ORR, PFS, and OS compared to the chemotherapy group. These findings align with those from the RCT study. Notably, our results showed that PFS and OS were better in the pembrolizumab group than in the chemotherapy group in the second or more line populations, with statistically significant results. This result was similar to the results of another published retrospective study, 22 which retrospectively analyzed consecutive patients treated with ICIs (nivolumab or pembrolizumab) as monotherapy at the Affiliated Hospital of Beijing Medical College, which showed promising actual clinical outcomes of ICIs in the treatment of second or more lines NSCLC patients. In addition to this, this research revealed that among patients with squamous carcinoma, the PFS and OS in the pembrolizumab cohort did not show a statistically significant difference compared to the chemotherapy group. This outcome diverges from the findings of the RCT study. The lack of significant difference could be attributed to the relatively limited sample size of patients with squamous carcinoma. PD-1/PD-L1 antibody inhibitors have become an important antitumor agent after surgery, radiotherapy, and chemotherapy due to their excellent clinical efficacy. 23 Compared with conventional chemotherapy, pembrolizumab has clinically significant efficacy, but its treatment cost is higher and needs further economic evaluation. In our research, we assessed the cost-effectiveness of pembrolizumab monotherapy and combination chemotherapy treatment compared to chemotherapy treatment, utilizing real-world data. We employed a partitioned survival model for this evaluation. The study period was the entire life cycle of the patients. Only direct medical costs were calculated for patients' treatment costs. The study results showed that the cost of treatment required to achieve a clinical benefit of 1 QALY was $ 187960.62 and $ 91886.97 for the pembrolizumab and chemotherapy groups, respectively, when the drug-grant policy was not considered. In contrast to the chemotherapy group, opting for pembrolizumab monotherapy or a combination of chemotherapy treatments can result in superior clinical benefits. The ICER for these treatments amounts to $ 146409.07/QALY, which significantly exceeds the threshold set at three times China's per capita GDP in 2022. Therefore, the investment in treatment costs to attain additional clinical benefits in the pembrolizumab group may not be considered cost-effective. When considering the drug donation policy, the pembrolizumab group is considered cost-effective with three times per capita GDP ( $ 64523.8) in Guangzhou as the threshold. Furthermore, the results of the DSA indicated that the primary influential factors on the outcomes were the cost of pembrolizumab, the utility value associated with PFS, and the utility value associated with PD. Comparing this study with RCT-based economics studies, this study was consistent with published economics studies concluding 24–27 that it is not economical in China. However, the study by Weng et al 28 showed that the pembrolizumab regimen was economical at a threshold of $ 180 000, regardless of PD-L1 TPS levels. In addition, the drug gift policy was considered in this study, and the pembrolizumab group was still not economical under the three times GDP per capita threshold when the drug gift policy was considered. Since the cost data in this study were mainly from Guangzhou, the pembrolizumab group was economical under the threshold of three times the per capita GDP in Guangzhou, thus, it can be seen that the economics of drugs can be improved to some extent under the premise of enjoying the complimentary drug policy in developed areas. This study has some limitations. First, it was a retrospective study conducted at a single center, focusing on patients with advanced NSCLC. Second, the follow-up period was not long enough to obtain data on overall survival. Third, biomarkers such as PD-L1 and TMB were not analyzed, which are currently under active investigation. Therefore, follow-up studies could further expand the sample size and supplement the efficacy studies in this patient population. Several economic limitations should be noted in this study. Firstly, the utility values related to health status were obtained from existing literature and did not differentiate between the variations in utility associated with different treatment approaches. Additionally, the DSA outcomes highlighted that the utility values corresponding to the absence of disease progression and the utility values related to disease progression played pivotal roles and could introduce bias into the results of the economic assessment conducted in this study. Therefore, there is an urgent need to further refine the measurement of utility values among different treatment modalities in the Chinese population in the future. 5 | CONCLUSIONS According to our comparison, our findings are similar to those of the RCT study and published retrospective studies. The Pembrolizumab group was superior to the chemotherapy group in the first-line population in terms of ORR, PFS, and OS, and also showed favorable clinical outcomes in second and more lines NSCLC patients. Regarding the economic aspect, our findings demonstrated that when considering a WTP threshold set at three times the GDP per capita ( $ 36070.2) in China, the pembrolizumab treatment group did not exhibit a cost-effectiveness advantage compared to the chemotherapy regimen in patients with advanced NSCLC. This economic disadvantage persisted even when considering the benefit of the drug-grant policy. However, the pembrolizumab group was economic when the threshold was set at three times the per capita GDP ( $ 64523.8) in Guangzhou, considering the drug-grant policy. The economics of pembrolizumab can be improved to some extent by considering lowering its price or using it in developed areas in the future. Declarations AUTHOR CONTRIBUTIONS Ning Wan: Conceptualization-Lead, Data curation-Lead, Funding acquisition-Lead, Methodology-Lead, Project administration-Lead, Resources-Lead, Supervision-Lead, Validation-Lead, Writing-review & editing-Lead. Chen Yang: Conceptualization-Lead, Data curation-Lead, Formal analysis-Lead, Project administration-Lead, Supervision-Lead, Writing-review & editing-Lead. Bing Wang: Conceptualization-Lead, Data curation-Lead, Methodology-Lead, Project administration-Lead, Software-Lead, Writing-original draft-Lead. ZiJian He: Data curation-Equal, Investigation-Equal, Methodology-Equal, Project administration-Equal, Software-Equal. YaJuan Lv: Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Writing-review & editing-Equal. LiQing Lu: Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Writing-review & editing-Equal. Ning Yang: Conceptualization-Equal, Data curation-Equal, Investigation-Equal, Methodology-Equal, Project administration-Equal, Supervision-Equal. WeiBin Xiao: Data curation-Equal, Formal analysis-Equal, Methodology-Equal, Supervision-Equal. YongBang Chen: Data curation-Equal, Formal analysis-Equal, Methodology-Equal, Software-Equal, Supervision-Equal. Jin Yuan: Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Funding acquisition-Lead, Writing – review & editing-Equal. DanDan Yang: Conceptualization-Equal, Data curation-Equal, Investigation-Equal, Resources-Equal, Supervision-Equal. Tao Liu: Conceptualization-Equal, Data curation-Equal, Project administration-Equal, Resources-Equal, Supervision-Equal. WenFeng Fang: Conceptualization-Equal, Data curation-Equal, Project administration-Equal, Supervision-Equal, Writing-review & editing-Equal. ZhuoJia Chen: Conceptualization-Equal, Data curation-Equal, Investigation-Equal, Project administration-Equal, Supervision-Equal, Writing-review & editing-Equal. WeiTing Liang: Conceptualization-Equal, Data curation-Equal, Methodology-Equal, Project administration-Equal, Resources-Equal, Supervision-Equal, Writing-review & editing-Equal. SUPPLEMENTARY INFORMATION Additional file 1. FUNDING INFORMATION This work was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515012251) and the Science and Technology Program of Guangzhou (No. 202002030446). CONFLICT OF INTEREST The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ETHICS APPROVAL STATEMENT The study received ethical approval from the Ethics Committee of Sun Yat-sen University Cancer Center in 2022 (Sun Yat-sen University Cancer Center Ethics Committee No. B2022-153-01. Consent to Participation Statement Every human participant agrees to participate. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Consent for publication Not applicable. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin . May 2021;71(3):209-249. doi:10.3322/caac.21660 Schenk EL, Patil T, Pacheco J, Bunn PA, Jr. 2020 Innovation-Based Optimism for Lung Cancer Outcomes. Oncologist . Mar 2021;26(3):e454-e472. doi:10.1002/onco.13590 Rossi A, Di Maio M. 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Cost-Effectiveness Analysis of Pembrolizumab Plus Pemetrexed and Platinum Versus Chemotherapy Alone as First-Line Treatment in Metastatic Non-Squamous Non-Small Cell Lung Cancer: A Reconstruction of Partitioned Survival Model Based on Time Dependent Pricing Mechanism of Patient Assistance Program. Front Oncol . 2021;11:768035. doi:10.3389/fonc.2021.768035 Shi Y, Chen W, Zhang Y, et al. Cost-effectiveness of pembrolizumab versus docetaxel as second-line treatment of non-small cell lung cancer in China. Ann Transl Med . Sep 2021;9(18):1480. doi:10.21037/atm-21-4178 Zhou K, Jiang C, Li Q. Cost-effectiveness analysis of pembrolizumab monotherapy and chemotherapy in the non-small-cell lung cancer with different PD-L1 tumor proportion scores. Lung Cancer . Oct 2019;136:98-101. doi:10.1016/j.lungcan.2019.08.028 Weng X, Luo S, Lin S, et al. Cost-Utility Analysis of Pembrolizumab Versus Chemotherapy as First-Line Treatment for Metastatic Non-Small Cell Lung Cancer With Different PD-L1 Expression Levels. Oncol Res . Mar 27 2020;28(2):117-125. doi:10.3727/096504019x15707883083132 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4254848","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291617229,"identity":"47882b64-92a8-47d1-ad6e-9d2ba01bcbce","order_by":0,"name":"Ning Wan","email":"","orcid":"","institution":"General Hospital of Southern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Wan","suffix":""},{"id":291617230,"identity":"7e3d6a9e-f61a-4134-9ee3-3dfddd60f981","order_by":1,"name":"Chen Yang","email":"","orcid":"","institution":"General Hospital of Southern Theater 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(A) overall population; (B) first-line treatment population; (C) non-squamous population; (D) squamous population. CI, confidence interval; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/640584c97cfd30f0ad1135be.png"},{"id":55316444,"identity":"06a07f9e-79c9-4519-b94d-1f847c2ff2cb","added_by":"auto","created_at":"2024-04-25 15:46:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117264,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of overall survival (OS) comparing the pembrolizumab group and chemotherapy group. (E) overall population; (F) first-line treatment population; (G) non-squamous population; (H) squamous population. CI, confidence interval; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/8ebc05daa9615886daa97d7b.png"},{"id":55316440,"identity":"ae22e0b5-00ca-4be7-a6b6-563d49ebfc82","added_by":"auto","created_at":"2024-04-25 15:46:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60852,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Kaplan-Meier curves between the pembrolizumab and chemotherapy groups in the second and more lines of treatment population. (A) Progression-free survival (PFS); (B) Overall survival (OS); CI: confidence interval; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/d5ec6759469e62f951ce90d5.png"},{"id":55319902,"identity":"e1a8ee09-e441-493b-a88b-75b1b3ccaeb5","added_by":"auto","created_at":"2024-04-25 16:02:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134840,"visible":true,"origin":"","legend":"\u003cp\u003eTornado chart for one-way sensitivity analysis (Free medication is not considered).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/355c6ec14cbd28e75e141459.png"},{"id":55316446,"identity":"0dad2a29-4fc3-4f42-8901-8ff1a8c4ad6c","added_by":"auto","created_at":"2024-04-25 15:46:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152625,"visible":true,"origin":"","legend":"\u003cp\u003eTornado chart for one-way sensitivity analysis (Consider Medication giveaways).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/364434af76e2d227703b1775.png"},{"id":55316445,"identity":"53439bfc-de3d-4932-99c0-a1097918bcd4","added_by":"auto","created_at":"2024-04-25 15:46:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":36152,"visible":true,"origin":"","legend":"\u003cp\u003eCurves of cost-effectiveness acceptability (Free medication is not considered).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/a40ad6ef859a633301809ea5.png"},{"id":55318065,"identity":"33205b57-b397-4405-8114-7ef009b85a0f","added_by":"auto","created_at":"2024-04-25 15:54:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43614,"visible":true,"origin":"","legend":"\u003cp\u003eCurves of cost-effectiveness acceptability (Consider Medication giveaways).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/6fc741a5d522298300007b32.png"},{"id":55905289,"identity":"8d07f9ea-09a9-478e-a462-16c59caf0ef9","added_by":"auto","created_at":"2024-05-06 06:40:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1655400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/1e1a7f1b-ebe0-4d3a-a541-176ee47d2c36.pdf"},{"id":55316441,"identity":"e3707316-0add-4b54-81f7-b82ffe7d37f3","added_by":"auto","created_at":"2024-04-25 15:46:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30505,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4254848/v1/26ff52c9ff821846a825341d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy for patients with advanced non-small cell lung cancer: a real-world study","fulltext":[{"header":"1 | INTRODUCTION","content":"\u003cp\u003eLung cancer remains the leading cause of cancer deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all lung cancer types.\u003csup\u003e1, 2\u003c/sup\u003e Conventional platinum-containing double combination chemotherapy is the standard first-line treatment for advanced NSCLC; however, its survival rate is low.\u003csup\u003e3, 4\u003c/sup\u003e In the past few years, significant advancements in lung cancer treatment have been achieved through the development of immune checkpoint inhibitors (ICIs), specifically targeting programmed death receptor 1 (PD-1) and its ligand PD-L1. These ICIs work by impeding the interaction between PD-1 and PD-L1, effectively hindering the proliferation of cancer cells. Pembrolizumab, one of the ICI drugs, is proven to be clinically effective for advanced NSCLC in multiple randomized controlled trials (RCTs) and has become one of the clinical treatment options.\u003c/p\u003e \u003cp\u003eDrawing on the results of the KEYNOTE-024 and KEYNOTE-010 studies,\u003csup\u003e5, 6\u003c/sup\u003e it was observed that pembrolizumab used alone significantly benefitted patients with advanced NSCLC who had not undergone previous treatments and had a PD-L1 tumor proportion score (TPS) of 50% or more. Following the outcomes of the KEYNOTE-024 clinical trial, pembrolizumab was established as the standard treatment for patients with NSCLC who have a PD-L1 TPS of 50% or higher. Moreover, in the KEYNOTE-189 and KEYNOTE-047 trials,\u003csup\u003e7, 8\u003c/sup\u003e the effectiveness of combining pembrolizumab with chemotherapy exceeded that of chemotherapy alone in treating patients with metastatic non-squamous and squamous NSCLC, demonstrating a higher degree of clinical benefit.\u003c/p\u003e \u003cp\u003eDespite the exciting results of this new immunotherapy. However, the high prices associated with these new therapies pose a significant challenge to the healthcare system. Relevant economic studies have shown that the current price tag of pembrolizumab is not cost-effective and that it needs to be reduced to be cost-effective compared to commonly used chemotherapy treatments.\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e However, all of the above studies are economic analyses based on the results of RCTs, and there is a noticeable scarcity of economic analyses based on outcomes from real-world studies.\u003c/p\u003e \u003cp\u003eWhile RCTs have shown the clinical benefits of using pembrolizumab alone or alongside chemotherapy in the management of advanced NSCLC, there may be some differences between the patients enrolled in RCTs and those in real-world situations due to its strict criteria. To date, there have been several real-world studies exploring the efficacy and safety of pembrolizumab, but few have been reported economically.\u003c/p\u003e \u003cp\u003eTherefore, it is also important to understand efficacy, safety, and economics in real-world clinical settings. Good observational studies can also provide some degree of evidence to complement clinical trials. This study aimed to assess the effectiveness, safety, and cost-effectiveness of pembrolizumab, both as a single-agent therapy and in combination with chemotherapy, compared to chemotherapy alone in treating advanced NSCLC in real-world settings.\u003c/p\u003e"},{"header":"2 | MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 | Study design and patients\u003c/h2\u003e \u003cp\u003eThis research involved a retrospective cohort analysis of advanced NSCLC patients treated with either pembrolizumab monotherapy or in combination with chemotherapy and chemotherapy at large 3A Hospital. The inclusion criteria were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 18 years or older, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Participants required a first-time diagnosis of stage III/IV NSCLC by imaging and pathological tissue, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) At least 1 measurable lung lesion, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) At least 2 weeks of treatment with pembrolizumab or conventional chemotherapy, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Routine baseline examinations must be performed during or before treatment with pembrolizumab or conventional chemotherapeutic agents: chest and abdomen scan\u0026thinsp;+\u0026thinsp;enhanced computed tomography (Computer Tomography, CT), complete biochemistry, routine blood work, cardiac enzymes, plasma adrenocorticotropic hormone, serum cortisol, two pancreatic enzymes and so on. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Patients receiving pembrolizumab monotherapy or in combination with chemotherapy and platinum-based chemotherapy. The exclusion criteria were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Individuals diagnosed with additional malignant tumors, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Participants who have an expected survival of less than 1 month, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Patient adherence to treatment with poor compliance and incomplete medical records, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Patients in the pembrolizumab group received multiple ICIs, such as the using of nivolumab, atezolizumab and so on, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Patients are treated with pembrolizumab along with other targeted agents.\u003c/p\u003e \u003cp\u003eFor cost-effectiveness analysis, a partitioned survival model was employed to forecast the anticipated expenses and results of pembrolizumab relative to chemotherapy. The model was developed as a partitioned survival model, comprising three distinct health states: progression-free (initial state), progressive disease, and death. The model cycling period in the study was set at three weeks consistent with the dosing cycle, and the model predicted time levels for the entire patient lifespan. In this research, both cost and utility data were subjected to a 5% discount rate for calculation purposes. The willingness-to-pay threshold for the Incremental Cost-Effectiveness Ratio (ICER) was established at three times the 2022 Gross Domestic Product (GDP) per capita in China, which was \u003cspan\u003e$\u003c/span\u003e36070.2. Given that the real-world study took place in Guangzhou, the 2022 per capita GDP of Guangzhou was similarly utilized as the willingness-to-pay threshold for the Incremental Cost-Effectiveness Ratio (ICER), amounting to \u003cspan\u003e$\u003c/span\u003e64523.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 | Outcomes\u003c/h2\u003e \u003cp\u003eTumor response was evaluated based on the RECIST 1.1 criteria,\u003csup\u003e12\u003c/sup\u003e which include categories such as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). The primary outcomes measured in this study were progression-free survival (PFS), overall survival (OS), and the Incremental Cost-Effectiveness Ratio (ICER). PFS was computed from the initiation of treatment until the occurrence of disease progression (as evaluated by RECIST 1.1 criteria), death from any cause, or the latest follow-up date (March 31, 2023). OS was calculated from the commencement of pembrolizumab treatment until the event of death from any cause or the most recent follow-up. ICER stands for the cost per quality-adjusted life year (QALY) gained. The secondary endpoints encompassed the objective response rate (ORR), which measures the percentage of patients achieving CR or PR as the best response, the disease control rate (DCR), which quantifies the percentage of patients with CR, PR, or SD as the best response, and the assessment of adverse events (AEs). AEs were evaluated by the National Cancer Research Institute Common Terminology Criteria for Adverse Events version 5.0.\u003c/p\u003e \u003cp\u003eTumor response was determined for eligible patients who underwent a minimum of 2 treatment cycles for tumor assessment. AEs were evaluated for all patients who completed at least one treatment cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 | Data collection\u003c/h2\u003e \u003cp\u003eThe study data were extracted from the Hospital His system, including (i) Data from patient admissions: name, age, gender, height, weight, BMI, and so on; (ii) Data from electronic medical records: smoking history, complaints, diagnosis, medication use, adverse reactions, and so on; (iii) Data from prescription information: drug type, dosage, period of validity, number of lines of treatment, and so on. The collected data was augmented and structured by incorporating the patient's prior treatment history, the Eastern Cooperative Oncology Group (ECOG) performance status (PS) score, pathological histological type, TNM staging, routine blood, complete biochemistry, tumor markers, imaging tests, personal disease history (presence of hypertension, diabetes mellitus, tuberculosis (hypertension, diabetes, tuberculosis, dyslipidemia, and so on) and other relevant information. Additionally, a propensity score matching analysis was performed by matching for gender, age, BMI, ECOG score, pathological histological type, combination therapy status, TNM stage, and the count of treatment lines for patients in both groups considered.\u003c/p\u003e \u003cp\u003e In terms of economic evaluation, the research was designed from the standpoint of the Chinese healthcare system, focusing solely on the analysis of direct medical expenses incurred by patients. Direct medical expenses encompass the following components: the expenses related to treatment drugs, examination costs, ancillary treatment costs, regular follow-up costs, the cost of subsequent drug treatment following disease progression, and expenditures associated with adverse drug events. The drug costs were obtained from the relevant drug prices published by the GPO platform of Guangdong Province, and the costs of supporting treatment, adverse drug, and relevant examinations were obtained from published literature. The treatment protocols administered to patients from the onset of treatment until the initial occurrence of disease progression, as well as the subsequent treatment protocols following disease progression, were derived from real-world data. The patient treatment regimens were then compiled and weighted to ascertain the contribution of each treatment regimen to the overall regimen within each treatment group; for patients who appeared disease progression but were not clear about the subsequent treatment regimen, the expenses associated with their subsequent treatment protocol defaulted to the cost of supportive treatment, and for patients who died while patients who developed disease progression, the cost of their follow-up regimen defaulted to \u003cspan\u003e$\u003c/span\u003e0. In addition, the study incorporated the expenses related to grade 3\u0026thinsp;~\u0026thinsp;5 adverse events that occurred in both groups and weighted them based on the observed incidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 | Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS software, specifically version 25.0, while the graphs were generated using GraphPad Prism, with version 9.0 employed for this purpose. Descriptive statistics were utilized to provide an overview of the patient's clinical characteristics. Continuous variables were presented using median and range, while categorical variables were represented through counts and percentages. Comparisons of baseline clinical characteristics between groups were conducted using the t-test. The chi-square test and Fisher's exact probability method (Monte Carlo, MC) for count data. A P-value less than 0.05 was deemed to indicate statistical significance. To reduce confounding bias between the pembrolizumab and chemotherapy groups, propensity score matching (PSM) was employed. PFS and OS were evaluated using the Kaplan-Meier method, and comparisons were made with the log-rank test.\u003c/p\u003e \u003cp\u003eWe conducted univariate and multivariate Cox proportional hazards regression analyses to identify potential prognostic biomarkers, examining the relationship between clinicopathological characteristics and both PFS and OS. Variables that demonstrated a p-value below 0.10 in univariate analysis were incorporated into the multivariate analysis. The results were presented as hazard ratios (HRs) with 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eIn the cost-effectiveness analysis, we extracted data from the Kaplan-Meier survival curves of PFS and OS using GetData Graph Digitizer software. Subsequently, these PFS and OS curves were fitted and extrapolated using R software (version 4.2.3) along with the SURVHE package. The optimal curve fit was determined based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), complemented by visual inspection. This study employed Deterministic Sensitivity Analysis (DSA) to evaluate the impact of variations in parameters such as drug pricing, utility values of no-progress and progress states, and discount rate within the model, ensuring the stability of the results. Additionally, Probabilistic Sensitivity Analyses (PSA) were conducted. During the DSA, parameter values underwent a\u0026thinsp;\u0026plusmn;\u0026thinsp;20% variation from their original values. For the Probabilistic Sensitivity Analysis (PSA), 1000 Monte Carlo simulations were executed, employing repeated sampling in line with the variables' range and distribution. Here, the Gamma distribution was applied to cost data, and the Beta distribution was utilized for utility values. The outcomes of these Monte Carlo simulations were then used to construct cost-effectiveness acceptability curves.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 | RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 | Characteristics of the patients\u003c/h2\u003e\n \u003cp\u003e630 patients with advanced NSCLC were identified through an electronic database as having received pembrolizumab (monotherapy or combination chemotherapy) and chemotherapy. Among these individuals, 169 patients received pembrolizumab treatment, either alone or in combination with chemotherapy, while 461 patients underwent chemotherapy. The baseline demographic, clinical, and pathologic characteristics of the 630 patients were displayed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe gender differences, TNM stage, pathological histology, the number of treatment lines (first/second-line treatment), and smoking status between the two groups before PSM were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After matching, the differences in other confounding variables between the two groups became statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\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\u003eBaseline characteristics and treatment details of NSCLC patients receiving pembrolizumab or chemotherapy.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBefore PSM (n\u0026thinsp;=\u0026thinsp;630)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAfter PSM (n\u0026thinsp;=\u0026thinsp;450)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients treated with pembrolizumab group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;169)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients treated with chemotherapy group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;461)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients treated with pembrolizumab group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients treated with chemotherapy group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\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\u003eAge, years- Median (Interquartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.0 (53.0\u0026ndash;67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.0 (53.0\u0026ndash;66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.0 (52.0\u0026ndash;67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.0 (55.0-66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, Median (Interquartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.8 (20.6\u0026ndash;25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.1 (20.8\u0026ndash;25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.8 (21.2\u0026ndash;25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.9 (20.8\u0026ndash;25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, n (%)\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050\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=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (79.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e316 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (78.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 (75.7%)\u003c/p\u003e\n \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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (24.3%)\u003c/p\u003e\n \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\u003eHistology subtype, n (%)\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050\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=\"char\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (17.6%)\u003c/p\u003e\n \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\u003eNon-Squamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (68.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e396 (85.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110 (73.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248 (82.4%)\u003c/p\u003e\n \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\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (1.1%)\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 \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\u003eTNM stage, n (%)\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050\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=\"char\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125 (74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 (83.1%)\u003c/p\u003e\n \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\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (11.6%)\u003c/p\u003e\n \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\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (5.3%)\u003c/p\u003e\n \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\u003eECOG PS at diagnosis, n (%)\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=\"char\"\u003e\n \u003cp\u003e0.817\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=\"char\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141 (83.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e394 (85.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125 (83.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257 (85.4%)\u003c/p\u003e\n \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\u0026ge;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (2.3%)\u003c/p\u003e\n \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\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (12.3%)\u003c/p\u003e\n \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\u003eSmoking status (%)\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050\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=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89 (52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80 (53.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153 (50.8%)\u003c/p\u003e\n \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\u003eNever smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129 (42.9%)\u003c/p\u003e\n \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\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (6.3%)\u003c/p\u003e\n \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\u003eThe number of treatment lines (%)\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050\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=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (66.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 (67.4%)\u003c/p\u003e\n \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\u0026ge;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 | Tumor response\u003c/h2\u003e\n \u003cp\u003eAfter PSM, a total of 149 patients were enrolled in the pembrolizumab group, 3 of whom had the best overall outcome of NE, and 301 patients were enrolled in the chemotherapy group, 1 of whom had the best overall outcome of NE. Within the pembrolizumab group, 71 patients experienced PR, and 68 had SD, resulting in an ORR of 48.63% and a DCR of 95.21%. In the chemotherapy group, there was 1 case of CR, 107 of PR, and 161 of SD, leading to an ORR of 36.00% and a DCR of 90.00%. There was a significant difference in ORR between the two groups (p\u0026thinsp;=\u0026thinsp;0.011). Detailed information about the effectiveness of each treatment regimen is presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eIn addition, we performed an overall efficacy analysis for patients treated in the first line. After excluding NE patients, 95 patients were enrolled in the pembrolizumab group and 205 patients were enrolled in the chemotherapy group. Consequently, after matching, the ORR for first-line treatment was 58.95% in the pembrolizumab group and 36.63% in the chemotherapy group (p\u0026thinsp;=\u0026thinsp;0.001), while the DCR was 97.89% and 91.09% (p\u0026thinsp;=\u0026thinsp;0.029), respectively. The pembrolizumab group demonstrated higher ORR and DCR compared to the chemotherapy group. Detailed data on the efficacy of each treatment regimen can be found in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In the population receiving second or subsequent lines of treatment, after excluding patients with NE, 51 patients were included in the pembrolizumab group and 98 in the chemotherapy group. The analysis indicated that the differences in ORR and DCR between the pembrolizumab and chemotherapy groups were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This data is detailed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eBest overall efficacy after matching in the overall population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment response, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePembrolizumab group (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChemotherapy group (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\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\u003eComplete response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0)\u003c/p\u003e\n \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\u003ePartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (48.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (35.67)\u003c/p\u003e\n \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 disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (46.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (53.67)\u003c/p\u003e\n \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\u003eProgressive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (10.33)\u003c/p\u003e\n \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\u003eObjective response rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (48.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease control rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (95.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270 (90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\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\u003eBest overall efficacy after matching in the first-line treatment population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment response, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePembrolizumab group (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChemotherapy group (n\u0026thinsp;=\u0026thinsp;202)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\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\u003eComplete response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0)\u003c/p\u003e\n \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\u003ePartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (57.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (36.14)\u003c/p\u003e\n \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 disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (54.45)\u003c/p\u003e\n \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\u003eProgressive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (8.91)\u003c/p\u003e\n \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\u003eObjective response rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (58.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (36.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease control rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (97.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (91.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBest overall efficacy after matching in the second and more lines treatment population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment response, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePembrolizumab group (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChemotherapy group (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\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\u003eComplete response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \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\u003ePartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (31.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (34.69)\u003c/p\u003e\n \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 disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (58.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (52.04)\u003c/p\u003e\n \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\u003eProgressive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (13.26)\u003c/p\u003e\n \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\u003eObjective response rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (31.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (34.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease control rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (90.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (86.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\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=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 | Survival analysis\u003c/h2\u003e\n \u003cp\u003eAt the final evaluation point, out of the total patients, 281 (63.00%) experienced disease progression. Specifically, in the pembrolizumab group, 91 patients (62.33%) had disease progression, while in the chemotherapy group, this number was 190 (63.30%). The median PFS was notably different between the two groups: patients in the pembrolizumab group had a median PFS of 15.5 months (95%CI: 11.8\u0026ndash;19.2), in contrast to the chemotherapy group, which had a median PFS of 8.8 months (95%CI: 7.5\u0026ndash;10.1). This difference was statistically significant, with a Hazard Ratio (HR) of 0.611 (95% CI, 0.483\u0026ndash;0.774; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as illustrated in Fig.\u0026nbsp;1A. For patients in the pembrolizumab group, the median OS had not been reached at the time of analysis. In contrast, for those in the chemotherapy group, the median OS was recorded at 26.2 months (95%CI: 0.483\u0026ndash;0.774). The difference in survival outcomes between the two groups was significant, with an HR of 0.532 (95%CI, 0.399\u0026ndash;0.709; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as depicted in Fig.\u0026nbsp;2E. Additionally, Supplementary Tables S1 and S2 present both univariable and multivariable analyses of factors influencing PFS and OS. However, these analyses revealed no factors significantly associated with PFS and OS, as all p-values were greater than 0.05.\u003c/p\u003e\n \u003cp\u003eIn first-line treatment, patients in the pembrolizumab group had a median PFS of 15.5 months (95%CI: 12.4\u0026ndash;18.6), compared to 9.6 months (95%CI: 7.5\u0026ndash;11.7) for those in the chemotherapy group. This resulted in an HR of 0.615 (95% CI, 0.458\u0026ndash;0.824; p\u0026thinsp;=\u0026thinsp;0.002), as shown in Fig. 1B. Regarding median OS, it had not been reached for patients in the pembrolizumab group, whereas it was 27.7 months (95% CI: 20.2\u0026ndash;35.2) for the chemotherapy group. The statistical significance of this difference in survival rates was underscored by an HR of 0.538 (95% CI, 0.378\u0026ndash;0.765; p\u0026thinsp;=\u0026thinsp;0.002), as illustrated in Fig. 2F. In second or more lines of treatment, the median PFS for patients receiving pembrolizumab was 11.7 months (95%CI: 4.7\u0026ndash;18.7), while for those undergoing chemotherapy, it was 7.7 months (95%CI: 7.0-8.4). This difference was statistically significant, with an HR of 0.600 (95%CI, 0.404\u0026ndash;0.893; p\u0026thinsp;=\u0026thinsp;0.013), as displayed in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA. As for the median OS, it had not been reached in the pembrolizumab group, in contrast to 25.6 months (95%CI: 22.1\u0026ndash;29.1) in the chemotherapy group. This outcome also demonstrated a significant difference, indicated by an HR of 0.025 (95%CI, 0.319\u0026ndash;0.866; p\u0026thinsp;=\u0026thinsp;0.025), which is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;In non-squamous patients, those treated with pembrolizumab experienced a median PFS of 17.4 months (95%CI: 15.0-19.8), compared to 8.8 months (95%CI: 7.0-10.6) for those in the chemotherapy group. This difference was significant, with an HR of 0.582 (95%CI: 0.446-0.760; p\u0026lt;0.001), as depicted in Figure 1C. Regarding median OS, it had not been reached for the pembrolizumab group, whereas it was 27.0 months (95%CI: 17.1-36.9) for the chemotherapy group. The difference in survival outcomes was noted with an HR of 0.476 (95%CI: 0.330-0.688; p=0.691), as shown in Figure 2G. For squamous patients, the median PFS was observed to be 10.0 months (95%CI: 4.2-15.8) in the pembrolizumab group and 8.0 months (95%CI: 5.1-10.9) in the chemotherapy group. The HR for this difference was 0.658 (95%CI: 0.392-1.108; p=0.100), as illustrated in Figure 1D. In terms of median OS, it was 30.0 months (95%CI: not reached [NR]) for the pembrolizumab group and 27.7 months (95%CI: 16.7-38.7) for the chemotherapy group, with an HR of 0.335 (95%CI: 0.367-1.440; p=0.335), as shown in Figure 2H.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 | Adverse Events\u003c/h2\u003e\n \u003cp\u003eAEs of any grade were observed in 119 patients (79.87%) in the pembrolizumab group and 261 patients (86.71%) in the chemotherapy group, as detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. AEs of grade 3 or higher were reported in 6 patients (4.03%) in the pembrolizumab group and 22 patients (7.31%) in the chemotherapy group. The most frequent AEs in the pembrolizumab group included anemia (52.35%), cough (20.80%), and leukopenia (19.46%). In contrast, the chemotherapy group primarily experienced anemia (69.77%) and leukopenia (20.60%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe adverse drug reactions of the two groups.\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\u003ePembrolizumab group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChemotherapy group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAny Grade, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAny Grade, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3, n (%)\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\u003eAny AE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (79.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261 (86.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (7.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\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\u003e78 (52.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210 (69.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutropenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (18.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (13.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeukopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (19.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (20.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThrombocytopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (9.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (9.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRash\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePruritus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (6.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecreased appetite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (12.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\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\u003e24 (16.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (9.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (20.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (15.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\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\u003e7 (4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeadaches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (7.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdema of the extremities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHengitysvaikeudet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.33)\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\u003e12 (8.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (13.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.33)\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\u003e5 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePneumonitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune-related pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\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\u003eAbbreviated terms: AE-adverse events; AST\u0026ndash;aspartate aminotransferase; ALT\u0026ndash;alanine aminotransferase.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 | Cost-effectiveness analysis\u003c/h2\u003e\n \u003cp\u003eThe K-M survival plots for PFS and OS in both the pembrolizumab and chemotherapy groups were derived from the dataset. These plots were tailored to individual patient data, and the corresponding fitted outcomes can be found in Additional file 1: Table S3. The best-fit distribution of the survival curves was judged based on the AIC value of each fitted distribution and the smaller the AIC value, the better the fit of the distribution. The best-fit distributions for the K-M survival curves of PFS and OS in both the pembrolizumab and chemotherapy groups were identified as log-normal distributions. The results of the fitted distribution parameters for each survival curve model are shown in Additional file 1: Table S4.\u003c/p\u003e\n \u003cp\u003eThe study encompassing 450 patients, which assessed the efficacy and safety of pembrolizumab and chemotherapy groups, compiled treatment regimens from the initiation of therapy to the first occurrence of PD. It also documented subsequent treatment strategies post-PD. The varieties of anticancer drugs utilized in these regimens are detailed in Additional file 1: Table S5. The cost of the relevant drugs was obtained from the Guangdong Provincial GPO platform. The expense for each cycle of various anticancer medications was determined using the treatment dosage guidelines for different drugs specified in the 2022 CSCO NSCLC treatment guidelines. This calculation was combined with the characteristics of a hypothetical patient, who has a Body Surface Area (BSA) of 1.80 m\u0026sup2;, weighs 65 kg, and has a Creatinine clearance rate (Ccr) of 90 ml/min/1.73 m\u0026sup2;. The upper and lower bounds of the drug costs were estimated by adjusting the drug cost by \u0026plusmn;\u0026thinsp;20%. A uniform Gamma distribution was selected for the cost variability of drug treatments.\u003c/p\u003e\n \u003cp\u003eDrawing from the findings of the aforementioned safety assessment, significant non-immune-related adverse reactions observed in both the pembrolizumab and chemotherapy groups include anemia, neutropenia, and thrombocytopenia. These will be factored into the cost calculation for adverse reactions, incorporating negative utility values for such reactions. The costs for prevention and treatment of adverse reactions, screening, and supportive care described above were obtained from published literature \u003csup\u003e13\u003c/sup\u003e and were shown in Additional file 1: Table S6.\u003c/p\u003e\n \u003cp\u003eThe study calculated utility values for patients without disease progression and those with disease progression, as well as negative utility values for three severe adverse reactions: anemia, neutropenia, and thrombocytopenia. Both the utility values for patients and the negative utility values for adverse reactions were sourced from literature \u003csup\u003e14\u003c/sup\u003e (Additional file 1: Table S7). All utility value data were subjected to \u0026plusmn;\u0026thinsp;20% as upper and lower limits of utility values, and Beta distribution was chosen.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e displays the outcomes of the cost-effectiveness analysis comparing the pembrolizumab group to the chemotherapy group, excluding the impact of any complimentary drug policy. For the chemotherapy group, the average cost per patient was \u003cspan\u003e$\u003c/span\u003e81784, with an average QALY of 2.63. In contrast, the pembrolizumab group experienced an enhanced clinical benefit of 1.69 QALY, but at an increased cost of \u003cspan\u003e$\u003c/span\u003e329021, resulting in an ICER of \u003cspan\u003e$\u003c/span\u003e146409.07/QALY when compared to the chemotherapy group.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the cost-effectiveness analysis results for the pembrolizumab group versus the chemotherapy group, considering the complementary drug policy. In this analysis, the average cost for the chemotherapy group was \u003cspan\u003e$\u003c/span\u003e59112 per patient, with a mean QALY of 2.63. The pembrolizumab group, on the other hand, showed a clinical benefit increase of 1.69 QALY but incurred a higher cost of \u003cspan\u003e$\u003c/span\u003e153893. This resulted in an ICER of\u0026nbsp;\u003cspan\u003e$\u003c/span\u003e56127.74/QALY in comparison to the chemotherapy group.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of cost-utility analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eFree medication is not considered\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOST\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQALYs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC/E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;COST\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;QALYs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICER\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\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91886.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePembrolizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187960.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146409.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsider medication giveaways\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63789.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePembrolizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84001.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56127.74\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\u003e3.6 | Sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eThe study conducted a DSA on several cost parameters including drug costs, utility values for two disease states, associated screening and treatment expenses, and utility values for adverse effects, with variations set at \u0026plusmn;\u0026thinsp;20% of their base values. The WTP threshold was set at three times China\u0026apos;s GDP per capita for the year 2022. The findings indicated that the price of pembrolizumab, the utility values associated with disease progression and non-progression, and the cost of CT scans significantly influenced the outcomes, both with and without the consideration of complementary drugs. These results are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. It was also noted that a reduction in the price of pembrolizumab would lead to a further decrease in the ICER values.\u003c/p\u003e\n \u003cp\u003eThis study conducted a PSA through 1000 Monte Carlo simulations, as shown in Fig.\u0026nbsp;6 and Fig.\u0026nbsp;7.\u003c/p\u003e\n \u003cp\u003eFrom the cost-effectiveness acceptability curves, it\u0026apos;s evident that when comparing the pembrolizumab group (without complimentary drugs) and the chemotherapy group, as well as the pembrolizumab group (with complimentary drugs) against the chemotherapy group, the chemotherapy group attains 100% acceptability at a cost threshold of approximately three times China\u0026apos;s GDP per capita (\u003cspan\u003e$\u003c/span\u003e36070.2), outperforming the pembrolizumab group. Additionally, in the pembrolizumab group with complimentary drugs, its cost-effectiveness acceptability curve relative to the chemotherapy group indicates that at a threshold of three times Guangzhou\u0026apos;s per capita GDP (\u003cspan\u003e$\u003c/span\u003e64523.8), pembrolizumab achieves 100% acceptability.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 | DISCUSSION","content":"\u003cp\u003eAlthough retrospective studies have previously confirmed the antitumor function of pembrolizumab in treating patients with advanced NSCLC, most of them were single-arm studies.\u003csup\u003e15\u0026ndash;18\u003c/sup\u003e Fewer retrospective studies have directly compared and analyzed pembrolizumab with chemotherapy. Hence, this study was undertaken to further elucidate the effectiveness of pembrolizumab in a practical clinical environment.\u003c/p\u003e \u003cp\u003eA real-world study revealed that the ORR for pembrolizumab combined with chemotherapy and for chemotherapy alone was 53.3% and 40.5%, respectively (p\u0026thinsp;=\u0026thinsp;0.410).\u003csup\u003e19\u003c/sup\u003e However, this study had limitations, including a small sample size of only 54 patients (37 in the chemotherapy group and 17 in the pembrolizumab plus chemotherapy group), and a significant gender disparity in the patient cohort. In our current study, we conducted PSM analysis to address differences in patient demographics and assessed the effectiveness of both the pembrolizumab and chemotherapy groups. Following PSM, the study included 450 patients, with 149 in the pembrolizumab group and 301 in the chemotherapy group. The ORR was 48.63% for the pembrolizumab group and 36.00% for the chemotherapy group, showing statistical significance in our study (p\u0026thinsp;=\u0026thinsp;0.011). Moreover, we conducted an additional analysis to compare the efficacy of first-line treatment versus second-line and subsequent treatments. The findings indicated that the ORR for first-line treatment with pembrolizumab and chemotherapy was 58.95% and 36.63%, respectively (p\u0026thinsp;=\u0026thinsp;0.001), while for second-line and beyond, the ORR was 31.37% for pembrolizumab and 34.69% for chemotherapy (p\u0026thinsp;=\u0026thinsp;0.684). These results suggest that pembrolizumab's effectiveness as a first-line treatment for patients with advanced NSCLC is more pronounced.\u003c/p\u003e \u003cp\u003eIn this research, a significant disparity in PFS was observed between the two groups in the general population, with an HR of 0.611 (95%CI, 0.483\u0026ndash;0.774; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median PFS for the pembrolizumab cohort was 15.5 months, surpassing the PFS outcomes reported in the clinical trial.\u003csup\u003e20\u003c/sup\u003e Furthermore, the median OS was not yet reached in the population treated with pembrolizumab. The overall population's chemotherapy group exhibited a median OS of 26.2 months. This extended OS duration in the chemotherapy patients, compared to earlier studies, could be attributed to the fact that several individuals in this group received second or subsequent lines of treatment that included a crossover with immune therapy agents like sintilizumab, camrelizumab, toripalimab, tislelizumab, or nivolumab. Previously, a retrospective study based on a Japanese population reported a median PFS of 18.4 months for first-line pembrolizumab monotherapy in NSCLC patients with PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50%, and a median OS not achieved.\u003csup\u003e21\u003c/sup\u003e This is similar to the results of our study.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis extended to comparing the performance of the pembrolizumab and chemotherapy groups in populations receiving first-line and second or more lines of treatment. We found that in the first-line patient cohort, the pembrolizumab group exhibited superior outcomes in terms of ORR, PFS, and OS compared to the chemotherapy group. These findings align with those from the RCT study. Notably, our results showed that PFS and OS were better in the pembrolizumab group than in the chemotherapy group in the second or more line populations, with statistically significant results. This result was similar to the results of another published retrospective study,\u003csup\u003e22\u003c/sup\u003e which retrospectively analyzed consecutive patients treated with ICIs (nivolumab or pembrolizumab) as monotherapy at the Affiliated Hospital of Beijing Medical College, which showed promising actual clinical outcomes of ICIs in the treatment of second or more lines NSCLC patients. In addition to this, this research revealed that among patients with squamous carcinoma, the PFS and OS in the pembrolizumab cohort did not show a statistically significant difference compared to the chemotherapy group. This outcome diverges from the findings of the RCT study. The lack of significant difference could be attributed to the relatively limited sample size of patients with squamous carcinoma.\u003c/p\u003e \u003cp\u003ePD-1/PD-L1 antibody inhibitors have become an important antitumor agent after surgery, radiotherapy, and chemotherapy due to their excellent clinical efficacy.\u003csup\u003e23\u003c/sup\u003e Compared with conventional chemotherapy, pembrolizumab has clinically significant efficacy, but its treatment cost is higher and needs further economic evaluation. In our research, we assessed the cost-effectiveness of pembrolizumab monotherapy and combination chemotherapy treatment compared to chemotherapy treatment, utilizing real-world data. We employed a partitioned survival model for this evaluation. The study period was the entire life cycle of the patients. Only direct medical costs were calculated for patients' treatment costs. The study results showed that the cost of treatment required to achieve a clinical benefit of 1 QALY was \u003cspan\u003e$\u003c/span\u003e187960.62 and \u003cspan\u003e$\u003c/span\u003e91886.97 for the pembrolizumab and chemotherapy groups, respectively, when the drug-grant policy was not considered. In contrast to the chemotherapy group, opting for pembrolizumab monotherapy or a combination of chemotherapy treatments can result in superior clinical benefits. The ICER for these treatments amounts to \u003cspan\u003e$\u003c/span\u003e146409.07/QALY, which significantly exceeds the threshold set at three times China's per capita GDP in 2022. Therefore, the investment in treatment costs to attain additional clinical benefits in the pembrolizumab group may not be considered cost-effective. When considering the drug donation policy, the pembrolizumab group is considered cost-effective with three times per capita GDP (\u003cspan\u003e$\u003c/span\u003e64523.8) in Guangzhou as the threshold. Furthermore, the results of the DSA indicated that the primary influential factors on the outcomes were the cost of pembrolizumab, the utility value associated with PFS, and the utility value associated with PD. Comparing this study with RCT-based economics studies, this study was consistent with published economics studies concluding \u003csup\u003e24\u0026ndash;27\u003c/sup\u003e that it is not economical in China. However, the study by Weng et al \u003csup\u003e28\u003c/sup\u003e showed that the pembrolizumab regimen was economical at a threshold of \u003cspan\u003e$\u003c/span\u003e180 000, regardless of PD-L1 TPS levels. In addition, the drug gift policy was considered in this study, and the pembrolizumab group was still not economical under the three times GDP per capita threshold when the drug gift policy was considered. Since the cost data in this study were mainly from Guangzhou, the pembrolizumab group was economical under the threshold of three times the per capita GDP in Guangzhou, thus, it can be seen that the economics of drugs can be improved to some extent under the premise of enjoying the complimentary drug policy in developed areas.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, it was a retrospective study conducted at a single center, focusing on patients with advanced NSCLC. Second, the follow-up period was not long enough to obtain data on overall survival. Third, biomarkers such as PD-L1 and TMB were not analyzed, which are currently under active investigation. Therefore, follow-up studies could further expand the sample size and supplement the efficacy studies in this patient population. Several economic limitations should be noted in this study. Firstly, the utility values related to health status were obtained from existing literature and did not differentiate between the variations in utility associated with different treatment approaches. Additionally, the DSA outcomes highlighted that the utility values corresponding to the absence of disease progression and the utility values related to disease progression played pivotal roles and could introduce bias into the results of the economic assessment conducted in this study. Therefore, there is an urgent need to further refine the measurement of utility values among different treatment modalities in the Chinese population in the future.\u003c/p\u003e"},{"header":"5 | CONCLUSIONS","content":"\u003cp\u003eAccording to our comparison, our findings are similar to those of the RCT study and published retrospective studies. The Pembrolizumab group was superior to the chemotherapy group in the first-line population in terms of ORR, PFS, and OS, and also showed favorable clinical outcomes in second and more lines NSCLC patients. Regarding the economic aspect, our findings demonstrated that when considering a WTP threshold set at three times the GDP per capita (\u003cspan\u003e$\u003c/span\u003e36070.2) in China, the pembrolizumab treatment group did not exhibit a cost-effectiveness advantage compared to the chemotherapy regimen in patients with advanced NSCLC. This economic disadvantage persisted even when considering the benefit of the drug-grant policy. However, the pembrolizumab group was economic when the threshold was set at three times the per capita GDP (\u003cspan\u003e$\u003c/span\u003e64523.8) in Guangzhou, considering the drug-grant policy. The economics of pembrolizumab can be improved to some extent by considering lowering its price or using it in developed areas in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNing Wan:\u003c/strong\u003e Conceptualization-Lead, Data curation-Lead, Funding acquisition-Lead, Methodology-Lead, Project administration-Lead, Resources-Lead, Supervision-Lead, Validation-Lead, Writing-review \u0026amp; editing-Lead. \u003cstrong\u003eChen Yang:\u003c/strong\u003e Conceptualization-Lead, Data curation-Lead, Formal analysis-Lead, Project administration-Lead, Supervision-Lead, Writing-review \u0026amp; editing-Lead. \u003cstrong\u003eBing Wang:\u003c/strong\u003e Conceptualization-Lead, Data curation-Lead, Methodology-Lead, Project administration-Lead, Software-Lead, Writing-original draft-Lead. \u003cstrong\u003eZiJian He:\u003c/strong\u003e Data curation-Equal, Investigation-Equal, Methodology-Equal, Project administration-Equal, Software-Equal. \u003cstrong\u003eYaJuan Lv:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Writing-review \u0026amp; editing-Equal. \u003cstrong\u003eLiQing Lu:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Writing-review \u0026amp; editing-Equal. \u003cstrong\u003eNing Yang:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Investigation-Equal, Methodology-Equal, Project administration-Equal, Supervision-Equal. \u003cstrong\u003eWeiBin Xiao:\u003c/strong\u003e Data curation-Equal, Formal analysis-Equal, Methodology-Equal, Supervision-Equal. \u003cstrong\u003eYongBang Chen:\u0026nbsp;\u003c/strong\u003eData curation-Equal, Formal analysis-Equal, Methodology-Equal, Software-Equal, Supervision-Equal. \u003cstrong\u003eJin Yuan:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Funding acquisition-Lead, Writing – review \u0026amp; editing-Equal. \u003cstrong\u003eDanDan Yang:\u0026nbsp;\u003c/strong\u003eConceptualization-Equal, Data curation-Equal, Investigation-Equal, Resources-Equal, Supervision-Equal. \u003cstrong\u003eTao Liu:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Project administration-Equal, Resources-Equal, Supervision-Equal. \u003cstrong\u003eWenFeng Fang:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Project administration-Equal, Supervision-Equal, Writing-review \u0026amp; editing-Equal. \u003cstrong\u003eZhuoJia Chen:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Investigation-Equal, Project administration-Equal, Supervision-Equal, Writing-review \u0026amp; editing-Equal. \u003cstrong\u003eWeiTing Liang:\u003c/strong\u003e Conceptualization-Equal, Data curation-Equal, Methodology-Equal, Project administration-Equal, Resources-Equal, Supervision-Equal, Writing-review \u0026amp; editing-Equal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515012251) and the Science and Technology Program of Guangzhou (No. 202002030446).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to\u0026nbsp;\u003c/p\u003e\n\u003cp\u003einfluence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eETHICS APPROVAL STATEMENT\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Ethics Committee of Sun Yat-sen University Cancer Center in 2022 (Sun Yat-sen University Cancer Center Ethics Committee No. B2022-153-01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participation Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvery human participant agrees to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, et al. 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Mar 27 2020;28(2):117-125. doi:10.3727/096504019x15707883083132\u003c/li\u003e\n\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":"pembrolizumab, non-small cell lung cancer, real-world, cost-effectiveness, chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4254848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4254848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e While pembrolizumab has demonstrated effectiveness in clinical trials for non-small cell lung cancer (NSCLC), its real-world efficacy and cost-effectiveness, particularly considering its high cost, remain uncertain. This study aimed to compare the clinical efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy in treating patients with advanced NSCLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this retrospective cohort study, advanced NSCLC patients treated with pembrolizumab (either as monotherapy or combined with chemotherapy) and chemotherapy alone were analyzed from April 2017 to March 2023 at a major 3A Hospital. Primary outcomes included progression-free survival (PFS), overall survival (OS), and the incremental cost-effectiveness ratio (ICER). Secondary outcomes were the objective response rate (ORR), disease control rate (DCR), and adverse events (AE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study involved 630 patients, with 169 in the pembrolizumab group and 461 in the chemotherapy group. Post propensity score matching (PSM), the sample size was 450 (149 in pembrolizumab, 301 in chemotherapy). Pembrolizumab showed a significantly higher ORR (48.63% vs. 36.00%, p\u0026lt;0.05) and comparable DCR (95.21% vs. 90.00%, p\u0026gt;0.05) compared to chemotherapy. The median PFS was longer with pembrolizumab (15.5 months vs. 8.8 months, p\u0026lt;0.001), and the median OS was not reached compared to 26.2 months in chemotherapy. In second-line treatments, pembrolizumab showed superior PFS and OS. From the perspective of the Chinese healthcare system, pembrolizumab was not cost-effective compared to chemotherapy at a willingness-to-pay threshold of $36,070.2/QALY but was cost-effective at three times the per capita GDP in Guangzhou.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Pembrolizumab demonstrates superior clinical efficacy over chemotherapy in a real-world setting for advanced NSCLC, with manageable AEs. Its cost-effectiveness varies by regional economic conditions and payment thresholds, suggesting potential economic feasibility in economically developed areas with drug grant policies.\u003c/p\u003e","manuscriptTitle":"Comparison of Efficacy, safety, and cost-effectiveness of pembrolizumab versus chemotherapy for patients with advanced non-small cell lung cancer: a real-world study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 15:46:32","doi":"10.21203/rs.3.rs-4254848/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":"58fe4da5-b48c-4b8a-9f9c-fc2a59b6f008","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-06T06:39:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 15:46:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4254848","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4254848","identity":"rs-4254848","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}