Evidence of pre-approval clinical trial supporting the granted conditional approval for novel cancer drugs in China between 2015 and 2022

Xingxian Luo; Xin Du; Lin Huang; Qixiang Guo; Xufeng Lv; Cen Wang; Haopeng Liu; Yue Zhou; Xuecai Xue; Zhuangqi Li; Jingwen Liu; Shein-Chung Chow; Yue Yang

doi:10.1016/j.eclinm.2023.102177

. 2023 Aug 24;63:102177. doi: 10.1016/j.eclinm.2023.102177

Evidence of pre-approval clinical trial supporting the granted conditional approval for novel cancer drugs in China between 2015 and 2022

Xingxian Luo ^a,^b,^c,ⁱ, Xin Du ^a,^b,ⁱ, Lin Huang ^e, Qixiang Guo ^a,^b, Xufeng Lv ^f, Cen Wang ^g, Haopeng Liu ^h, Yue Zhou ^e, Xuecai Xue ^e, Zhuangqi Li ^a,^b, Jingwen Liu ^a,^b, Shein-Chung Chow ^d,^∗∗, Yue Yang ^a,^b,^∗

PMCID: PMC10474375 PMID: 37662522

Summary

Background

Accelerated approval (AA) of novel anticancer drugs based on surrogacy has attracted considerable concern globally. China National Medical Products Administration (NMPA) also established a similar conditional approval (CA) program to accelerate the approval of novel drugs to address unmet medical needs. This cross-sectional study aimed to evaluate the pre-approval clinical trial evidence and potential challenge of cancer drugs receiving CA in China from policy implementation to 2022.

Methods

The cancer drugs (initial and supplemental indications) granted CA between January 1, 2015 and December 31, 2022 using the public database of the NMPA were analyzed. The characteristics of the cancer drugs received CA were described. Primary efficacy endpoints and safety derived from the pre-approval clinical trial, including response rates (RR), progression-free survival (PFS), overall survival (OS), treatment-related serious adverse events (SAE) and Grade ≥3 adverse events (AEs) were quantitatively estimated by meta-analysis. Besides, the correlation between the surrogate endpoints and OS was estimated by the reported trial-level correlation analysis.

Findings

The NMPA approved 72 cancer indications (56 new molecular entities) with CA between 2015 and 2022. 34 indications (47%) were also approved by the FDA or EMA. 74% (53/72) of cancer indications were based on a single-arm trial design while 26% (19/72) for randomized controlled trials. The pooled RR was 0.50 (95% CI: 0.45–0.55, I² = 96%) with significant differences across cancer types and targets while the pooled hazard risk was 0.39 (95% CI: 0.28–0.53, I² = 89%) for PFS and 0.67 (95% CI: 0.61–0.73, I² = 0%) for OS. The pooled treatment-related SAE and Grade ≥3 AEs from single-arm designs resulted in 15% and 25%, respectively. In randomized controlled trials, the pooled treatment-related SAE and Grade ≥3 AEs observed in CA drugs and the control groups were comparable. Surrogate endpoints were widely used as the primary efficacy endpoints in the pre-approval pivotal clinical trials with 75% (54/72) for RR, 10% (7/72) for PFS, and 4% (3/72) for others. Of these, 27% (17/63) of the surrogate endpoints reported a trial-level correlation with OS; three reported high correlation (r ≥ 0.85), two reported moderate correlation (0.70 ≤ r < 0.85) and 12 reported low correlation (r < 0.70).

Interpretation

The majority of novel cancer drugs that received CA were based on RR designed for single-arm trials. The reported correlations of treatment effect between the surrogate endpoints and OS used for CA were limited. Our findings highlighted that the introduction of OS or quality of life based on RCT in confirmatory clinical trials as much as feasible was essential to ensure the clinical benefits for patients.

Funding

This study was supported by postdoctoral fellowship from Tsinghua-Peking Joint Centers for Life Sciences (CLS).

Keywords: Accelerated approval, Conditional approval, Overall survival, Surrogate endpoints, China

Research in context.

Evidence before this study

Accelerated approval, first established by the US Food and Drug Administration (FDA), is based on a surrogate endpoint that is "reasonably likely to predict" true clinical efficacy (i.e., survival or quality of life). It has become a common international practice to expedite the approval of cancer drugs to address serious diseases with unavailable treatments. However, uncertainty about the clinical benefit of accelerated approval of cancer drugs has caused widespread controversy, especially concerning the clinical trial evidence and strength of surrogate endpoints. China has also established a conditional approval (CA) program (similar to accelerated approval) to expedite novel drug launches, primarily in the field of cancer drugs in recent years. It is unclear whether the implementation of the conditional approval program in China may also involve these challenges.

Added value of this study

We included cancer indications with conditional approval by the China National Medical Products Administration (NMPA) from 2015 to 2022. Evidence from pre-approval pivotal clinical trials for these cancer indications. 72 cancer indications received conditional approval in China with the majority of pre-approval pivotal clinical trials in a single-arm design and response rate as primary efficacy endpoints. The response rate varied significantly across cancer types and drug classes. Correlations with overall survival (OS) were reported for 27% (17/63) of the surrogate endpoints, of which 3 were highly correlated, 2 were moderately correlated and 12 were low correlated. These results support the significance of establishing a correlation between surrogate endpoints and OS.

Implications of all the available evidence

The magnitude and correlations of surrogate endpoints (vs. OS) varied widely across cancer types and drug classes. Establishing the correlation between treatment effects of surrogate endpoints and treatment effects of OS for different drug classes in specific cancer types may be warranted for the success of CA programs.

Introduction

Patients with serious cancer have a desire for medicines that can directly improve their survival, symptoms or function.¹^,² Overall survival (OS) has been recognized by regulatory agencies as the gold standard globally in clinical decision-making for approval of cancer drugs.¹^,³^,⁴ However, there are several important challenges to the adoption of OS, including being time-consuming, subject to later crossover treatments, and the infeasibility of conducting randomized trials due to the inability to recruit enough patients.³^,⁵^,⁶ The U.S. Food and Drug Administration (FDA) first created an accelerated approval pathway primarily in response to the AIDS crisis in 1992.⁷ Subsequently, the FDA used accelerated approval (AA) primarily for cancer, allowing the use of surrogate endpoints that likely reasonably predict clinical benefits to expedite the approval of drugs for serious and life-threatening diseases.⁸ It also requires AA drugs to complete confirmatory clinical trials within a specified time frame to determine whether to convert to regular approval or withdraw from the market.⁸ Subsequently, the European Union (EU) established a conditional marketing authorization (CMA) in 2006 similar to the accelerated approval of the FDA.⁹ In the past few years, AA or conditional marketing authorization was predominantly granted for cancer drugs.¹⁰ The previous study conducted by the FDA in 2018 showed that 64 cancer agents with 93 indications were granted AA in the past 25 years, and only a small percentage of indications fail to verify clinical benefit, indicating that the program has been successful in delivering clinical benefit to patients.⁵

Based on the latest statistics from the World Health Organization, China ranked the highest cancer incidence and mortality rate in the world indicating cancer has been the leading cause of health burden in China.11, 12, 13 However, it should be acknowledged that the majority of Chinese cancer patients were not accessible to cutting-edge treatments prior to 2015, especially for targeted therapies, given the significant drug lags and outrageous costs.¹⁴ Fortunately, China initiated drug regulatory reforms in 2015, including the establishment of expedited programs, streamlined investigational new drug applications, and implementation of a patent term extension system, which has greatly facilitated the launch and development of cancer drugs in China.14, 15, 16, 17 Specifically, China initiated a conditional approval (CA) program pilot since 2015 (Fig. 1), implying that the novel drugs can be approved earlier on the basis of the surrogate endpoint or intermediate clinical endpoints, similar to the FDA’s AA and the EMA’s CMA programs.18, 19, 20 After several years of development, the CA program was officially incorporated into the law on drug and vaccine management in 2019 since this program has been playing an increasingly significant role in addressing serious life-threatening diseases for which there are no effective treatments, particularly in cancers (Fig. 1).¹⁴^,¹⁷

Fig. 1 — **Timeline of conditional approval in China’s Drug Administration.** CFDA, China Food and Drug Administration (Name changed to NMPA in September, 2018); NMPA, National Medical Products Administration.

Recently, many studies raised concerns about several fronts of the FDA accelerated approval program, including the uncertainty in the use of surrogate endpoints, the increasing use of the single-arm trials, withdrawal of numerous AA drugs that failed to complete confirmatory clinical studies as required.21, 22, 23, 24 China may also confront these challenges as the application of CA in the cancer field gradually increases. However, there exists limited evidence regarding conditional approval for cancer indications in China. Thereby, the aim of our study was to evaluate the pre-approval clinical trial evidence and potential challenge of cancer drug indications granted in China from its inception to 2022.

Methods

Data sources

We searched the official China NMPA database to include novel cancer drugs granted CA between January 1, 2015 and December 31, 2022.²⁵ These CA cancer indications were also searched in the FDA and EMA publicly available databases to determine if they were approved by the FDA²⁶ and EMA.²⁷ The data for the correlation between the treatment effects of the surrogate endpoints and treatment effects of the OS through published systematic reviews and meta-analyses similar to the previous study.²⁸ The source of the pooled efficacy and safety data was from the published literature.

Data extraction

Based on the NMPA review report and the latest labeling, we identified CA for all cancer drug indications (including the initial and supplement indications). This study included patent-protected small molecule entities and biologics for cancer drugs, while excluding traditional Chinese medicine, vaccines, adjuvant therapies (e.g., for the treatment of cancer complications), and contrast agents from the analysis. The characteristics of each CA indication were extracted, including the origin of manufacture, cancer type, dosage, biological mechanism, treatment line, date of initial and supplement approval. As with our previous study, cancer drugs introduced from abroad by domestic manufacturers were defined as imported drugs (e.g., duvelisib).²⁹ All cancer drugs were classified as small molecule targeted drugs, biologics, immunotherapy and cellular therapies. Furthermore, we distinguished between those indications that were approved only in China and those were also approved by the FDA and EMA. Two reviewers extracted the above information separately, and a third reviewer coordinated to resolve any inconsistencies.

Ethics

We used only publicly available information and pooled clinical trial-level data, consistent with the previous study.³⁰ Therefore, Institutional Review Board approval was not required. The data for this study were derived from the literature and review reports, thus it did not involve patients signing informed consent.

Identification of pivotal trials

We identified all of the pivotal clinical trials used to support each conditional approval of cancer indication from the NMPA review reports (the pivotal clinical trials are clearly described in the NMPA review reports). Considering that some review reports of cancer drugs had not yet been published, we identified the peer-review publications for their pivotal clinical trials based on the most recent label issued by the NMPA similar to the previous study.³¹ In addition, we also conducted a search of ClinicalTrials.gov to supplement data on pivotal clinical trials of these cancer drugs. To assess the primary efficacy endpoints employed to support these cancer agents, we used meta-analysis to perform a pooled analysis of overall survival (OS) and progression-free survival (PFS) reported in randomized controlled trials; response rates (RR) were pooled in single-arm trials. Similarly, treatment-related serious adverse reactions (SAE) and Grade ≥3 AEs from single-arm trials and randomized controlled trials (RCT) were pooled by meta-analysis to assess the safety of CA indications, respectively.

For solid cancers, RR was also deemed as objective response rates (ORR), consisting of partial response (PR) and complete response (CR) rates in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST).³²^,³³ For hematologic malignancies, RR was often calculated using the CR and PR, including cytogenetic testing, molecular responses, complete blood counts and serologic testing used to support NMPA approval similar to the previous study.³⁴ The standard for assessing RR in hematologic cancers was based on the use for various types of cancers. For example, RR for lymphoma was usually assessed according to the Lugano 2014 Classification while myeloma was assessed using the International Myeloma Working Group (IMWG) criteria.

Correlation between surrogate endpoints and OS

To assess the strength between each surrogate endpoint (e.g., response rate and progression-free survival) and overall survival, we identified the trial-level studies (level-1 evidence) that reported validating their relationship. Typically, trial-level analyses are considered the highest level of evidence to establish the validity of surrogate endpoints.³⁵^,³⁶ We excluded individual-level correlation analyses (level-2 evidence) and non-randomized controlled trials. Similar to the previous study,²⁸ only trial-level surrogate endpoint correlation analyses for regulatory use were included, which indicated the need to analyze the correlation between treatment effects for surrogate endpoints and treatment effects for OS. The types of correlation analyses included as follows: (a) correlation between the hazard ratio (HR) of time-to-event surrogate (e.g., PFS and TTP) and the HR of OS; (b) correlation between the odds ratio of response rate and the HR of OS; (c) correlation between the median difference/ratio in time-to-event surrogate (experimental group minus control group or the ratios between experimental group and control group) and median difference in OS (experimental group minus control group or the ratios between experimental group and control group).

Several recent studies have been performed to systematically review the correlation between surrogate endpoints and OS in all cancers.²⁸^,³¹^,³⁷ Therefore, we supplemented these results with an additional search of the literature until April 1, 2023, for correlation analysis between surrogate endpoints and OS in PubMed. The correlation coefficients or R², indications, number of included studies, lines of treatment, and biological mechanisms between the surrogate endpoints and OS reported in these studies were extracted similarly to the previous report.³¹ The detailed search strategies are provided in eTable S1. To better demonstrate the trial-level correlation between HR of time-to-event surrogate and HR of OS, we plotted their scatter plots based on the published studies. The correlation coefficients between the surrogate endpoints and OS greater than 0.85, 0.70–0.85 and less than 0.70 were considered as high, moderate and low correlations, respectively, which were consistent with the previous studies.³¹^,36, 37, 38 Then, we evaluated the correlation between surrogate endpoints and OS for CA cancer indications based on similar cancer indications and primary efficacy endpoints by the median correlation coefficients collected above.

Statistics

Continuous variables were expressed as medians (interquartile range, IQR), the while categorical variables were presented as figures (percentages). The fisher's exact test was used for categorical variables. The Mann–Whitney U test was used to determine the analysis of differences between the two groups. We performed a pooled analysis using meta-analysis for the primary efficacy endpoints and safety outcome (HR for PFS and OS reported in RCT; RR for single-arm trials; treatment-related SAE and Grade ≥3 AEs from the RCT or single-arm design), which was similar to the previous studies.³⁰^,³⁹^,⁴⁰ Statistical heterogeneity was assessed using the I² statistic (calculated by Cochran's Q [100 × (Q-df ÷ Q)]) and χ² (P < 0.01) to assess between-trial heterogeneity.⁴¹ Usually, I² values ≥50% or P < 0.1 indicated significant inter-trial heterogeneity, and the pooled results were evaluated by random-effects models; otherwise, fixed-effects model was performed.⁴² To identify heterogeneity between trials for PFS, OS, RR, treatment-related SAE and Grade ≥3 AEs, we performed a subgroup analysis. Sensitivity analyses were performed to assess the impact of significant heterogeneity on the pooled study results by systematically excluding each individual study from the analysis. Additionally, the Begg's and Egger's tests were employed to evaluate potential publication bias among the included studies. Statistical analyses and graphs were conducted using SPSS 20.0 and R, version 4.1.0 (R package meta, version 5.2-0; R package forestplot, version 1.10.1 and R package ggplot2, version 3.4.0). P < 0.05 was interpreted as a significant difference.

Role of the funding source

The funder had no role in the design of the study, data collection, data analysis, interpretation, or the writing of the paper. All authors have full access to all data in the study and agree to submit the manuscript for publication.

Results

Characteristics of CA cancer drugs and pivotal trials

We identified 60 new cancer drugs that were granted CA by the NMPA between January 2015 and December 2022. After exclusion (two for not NMEs; one for traditional Chinese medicine; one for supportive therapy), 56 NMEs with 72 indications were included (Table 1 and eTable S2). Of these 72 indications, 48 (67%) were for the treatment of solid cancers. The most common type of cancer was lymphoma (22%), followed by lung cancer (14%), leukemia (7%) and solid cancers (7%). CA indications were granted primarily for initial New Drug Applications (NDA) or Biologic license applications (BLA), accounting for 51% and 26%, respectively. Small molecule targeted drugs (58%) and immunotherapy (36%) were the primary drug classes for the CA cancer indications. The majority of obtained CA cancer drugs (79%) are used for late-stage treatment. 65% (47/72) of the indications approved for cancers included the Chinese mainland population (Table 1).

Table 1.

Characteristics of the cancer drugs granted the conditional approval by the NMPA, 2015–2022.

Characteristics	Indications, No. (%)			P value
	All (n = 72)	Pivotal trial design
	All (n = 72)	Domestic drugs (n = 41)	Imported drugs (n = 31)
Cancer type
Solid	48 (67)	26 (54)	22 (46)	0.616
Hematologic malignancies	24 (33)	15 (63)	9 (37)
Market authorization
Approved in China only	39 (54)	39 (100)	0 (0)	<0.0001
Also approved by the FDA or EMA	33 (46)	2 (6)	31 (94)
Lines of therapy
First-line	13 (18)	3 (23)	10 (77)	0.014
Late-stage	57 (79)	36 (63)	21 (37)
Neoadjuvant	2 (3)	2 (100)	0 (0)
Chinese mainland population
Included	47 (65)	41 (87)	6 (13)	<0.0001
No included	25 (35)	0 (0)	25 (100)
Primary efficacy endpoints
RR	54 (75)	38 (69)	16 (31)	0.0075
PFS	7 (10)	2 (29)	5 (71)
OS	4 (6)	0 (0)	4 (100)
OS and PFS	3 (4)	1 (33)	2 (67)
OS and RR	1 (2)	0 (0)	1 (100)
MFS	2 (3)	0 (0)	2 (100)
Others	1 (1)	0 (0)	1 (100)
Drug mechanism class
Small molecule targeted agent	42 (58)	20 (48)	22 (52)	0.153
Biologic therapy	2 (3)	2 (100)	0 (0)
Immunotherapy	26 (36)	17 (65)	9 (35)
Cellular	2 (3)	2 (100)	0 (0)
Drug types
NDA	37 (51)	16 (43)	21 (57)	0.070
BLA	19 (26)	12 (63)	7 (37)
sNDA	4 (6)	3 (75)	1 (25)
sBLA	12 (16)	10 (83)	2 (17)
Trial designs
Single-arm	53 (74)	36 (68)	17 (32)	0.0026
Randomized controlled trial	19 (26)	5 (26)	14 (74)
Cancer site
Lymphoma	16 (22)	13 (81)	3 (19)	0.040
Lung cancer	10 (14)	5 (50)	5 (50)
Solid tumors	5 (7)	4 (80)	1 (20)
Leukemia	5 (7)	1 (20)	4 (80)
Thyroid cancer	4 (6)	1 (25)	3 (75)
Prostate cancer	4 (6)	1 (25)	3 (75)
Urothelium carcinoma	3 (4)	3 (100)	0 (0)
Myeloma	3 (4)	0 (0)	3 (100)
Breast cancer	3 (4)	2 (67)	1 (33)
Others	19 (26)	11 (58)	8 (42)

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NDA, new drug application; BLA, biologics license application; sNDA, supplement new drug application; sBLA, supplement biologics license application; OS, overall survival; PFS, progression-free survival; RR, response rate; MFS, metastasis-free survival; NMPA, National Medical Products Administration.

Fig. 2 reveals a trend of increasing indications for CA cancer granted by the NMPA in recent years. 38 (53%) of the cancer indications were approved in China only, and these were all developed by local companies. 32 (44%) and 30 (42%) cancer indications were also approved by the FDA and EMA, respectively (Table 2 and eTable S2). Of the FDA-approved indications, 59% (19/32) and 41% (13/32) received AA and regular approval, respectively (eTable S2). For the EMA-approved indications, 40% (12/30) and 60% (18/30) received CMA and regular approvals, respectively. Additionally, we identified 72 pivotal clinical trials for these CA indications, all of which were published in journals or meetings (eTable S2). These pivotal clinical trials were primarily single-arm trial designs (72%) (Table 1). Across the 72 pivotal clinical trials, RR was the primary endpoint (75%), followed by PFS (10%), OS (6%) and OS combined with PFS (4%) (Table 1).

Table 2.

Analysis of the included cancer drugs also approved by the FDA or EMA (n = 34).

Generic name	Indications	Initial approval time in China	Approval status in the FDA^a		Approval status in the EMA^a
Generic name	Indications	Initial approval time in China	Initial approval	Initial approval date	Initial approval	Initial approval date
Zanubrutinib	Mantle cell lymphoma	2020-06-02	AA	2019-11-14	NA	NA
Zanubrutinib	Chronic lymphocytic leukemia	2020-06-02	NA	NA	RA	2022-10-13
Zanubrutinib	Waldenstrom macroglobulinemia	2021-06-16	RA	2021-08-31	RA	2021-11-22
Entrectinib	Solid cancer	2022-07-26	AA	2018-08-15	CMA	2020-07-31
Dabrafenib	Lung cancer	2022-03-24	RA	2017-06-22	RA	2013-08-26
Ivosidenib	Leukemia	2022-02-09	RA	2018-07-20	Withdrawal	NA
Pemigatinib	Cholangiocarcinoma	2022-04-06	AA	2020-04-17	CMA	2021-03-26
Larotrectinib	Solid cancer	2022-06-23	AA	2018-11-26	CMA	2019-09-19
Sacituzumab	Breast cancer	2022-06-07	AA	2020-04-22	RA	2021-11-22
Duvelisib	Lymphoma	2022-03-16	AA	2018-09-24	RA	2021-05-19
Blinatumomab	Leukemia	2022-04-27	AA	2014-12-03	CMA	2015-11-23
Selinexor	Myeloma	2021-12-14	AA	2019-07-03	CMA	2021-03-26
Ipilimumab	Mesothelioma pleura	2021-06-08	RA	2020-10-02	RA	2021-04-22
Pralsetinib	Lung cancer	2021-03-23	AA	2020-09-04	CMA	2021-11-18
Pralsetinib	Thyroid cancer	2022-03-08	AA	2020-12-01	NA	NA
Ripretinib	Gastrointestinal stromal tumor	2021-03-30	RA	2020-05-15	RA	2021-11-18
Avapritinib	Gastrointestinal stromal tumor	2021-03-30	RA	2020-01-09	CMA	2020-09-24
Gilteritinib	Leukemia	2021-01-30	RA	2018-11-28	RA	2019-10-24
Venetoclax	Leukemia	2020-12-02	AA	2018-11-21	CMA	2016-04-12
Carfilzomib	Myeloma	2021-07-06	AA	2012-07-20	RA	2015-11-19
Pembrolizumab	Head and neck cancer	2020-12-08	AA	2016-08-05	RA	2019-10-17
Pembrolizumab	Colorectal cancer	2021-06-08	AA	2017-05-23	RA	2020-12-10
Olaparib	Prostate cancer	2021-06-16	RA	2020-05-19	RA	2020-09-17
Dinutuximab Beta	Neuroblastoma	2021-08-12	NA	NA	RA	2017-05-08
Apalutamide	Prostate cancer	2019-09-05	RA	2018-02-14	RA	2019-01-14
Atezolizumab	Lung cancer	2021-04-27	RA	2020-05-18	RA	2019-07-25
Daratumumab	Myeloma	2019-07-04	AA	2015-11-16	CMA	2016-05-20
Pralatrexate	Lymphoma	2020-08-26	AA	2014-07-03	Refused	NA
Selpercatinib	Lung cancer	2022-10-09	AA	2020-05-08	CMA	2021-02-11
Selpercatinib	Thyroid cancer	2022-10-09	AA	2020-05-08	CMA	2021-02-11
Darolutamide	Prostate cancer	2021-02-02	RA	2019-07-30	RA	2020-03-27
Lenvatinib	Thyroid cancer	2020-11-04	RA	2015-02-13	RA	2015-05-28
Mogamulizumab	Granuloma fungoides	2022-10-28	RA	2018-08-08	RA	2018-11-22
Lorlatinib	Lung cancer	2022-04-27	AA	2018-11-02	CMA	2019-05-06

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AA, accelerated approval; CMA, conditional marketing authorization; RA, regular approval; NA, not available; FDA, Food And Drug Administration; EMA, European Medicines Agency.

We searched for FDA or EMA approval for these drugs with cut-off date of December 31, 2022.

Among the 72 CA cancer, there were 41 indications produced by domestic manufacturers and 31 imported ones. The majority of domestic cancer drugs were only available in China (except for three indications of zanubrutinib). In regards to the number of treatment lines, the proportion of imported cancer drugs used in first-line treatment was higher than that of domestic ones (77% vs. 23%). Among the drugs included the Chinese mainland population 41 (87%) and 6 (13%) are domestic and imported cancer, respectively (Table 1). In regards to clinical trial design, the proportion of domestic cancer drugs using single-arm trials was significantly higher than that of imported cancer drugs (68% vs. 32%, P = 0.0026). Additionally, domestic cancer drugs were most used for treatment of the lymphoma (13/41), while imported drugs for lung cancer (5/31). No significant differences in cancer types, drug mechanism class and drug types were observed between domestic and imported drugs (Table 1).

The characteristics of domestic (n = 5) and imported (n = 14) novel drugs using RCTs design are shown in eTable S3. The median number of patients included in domestic drugs conducted with RCT design was lower than with imported drugs despite not reaching a statistical difference (128 vs. 554, P = 0.064). All RCTs of domestic drugs included the Chinese mainland population while only 21% (3/14) for imported drugs. Additionally, all of the RCT-designed imported drugs were conducted in multi-region clinical trials (MRCTs), while the domestic drugs were conducted only in China. No significant differences were observed between domestic and imported drugs conducted with RCT design in relation to the type of control group (P = 0.603), blinding types (P = 0.603), primary efficacy endpoints (P = 0.143), drug types (P = 0.155), lines of therapy (P = 0.222) and cancer site (P = 0.223).

Efficacy of CA cancer drugs

The median RR employing a single-arm trial design to support the CA of cancer drugs was 55% (IQR: 30%, 75%). Of all the CA approved with RR, 6% (3/52) of RR was less than 20%, 25% (13/52) of RR was less than 30% and 46% (28/52) of RR was less than 60%. The pooled meta-analysis of RR was 50% (95% CI: 0.45, 0.55; I² = 96%) (Table 3 and Fig. 3). We further explored the magnitude of RR in regard to the origin of drugs, Chinese mainland population (included or not included), drug classes, cancer sites, drug types and cancer types (Table 3 and eTable S4). The results showed that the pooled RR of hematological cancer was significantly greater than that of solid cancer (62% vs. 43%; P < 0.0001). Similarly, the median RR of hematologic cancers was significantly higher than that of solid cancers (77% vs. 48%; P = 0.0047) (eFigure S1). The pooled RR of small molecule agents was significantly higher than biological products (60% vs. 39%; P = 0.0003) (Table 3). No significant difference in the pooled RR was comparable between domestic and imported cancer drugs (50% vs. 50%; P = 0.912) (eTable S4).

Table 3.

Treatment outcome for response rate, progression-free survival and overall survival.

Outcome	Overall		Cancer type				Drug types
	Overall		Solid tumor		Hematological cancer		NDA		BLA
	Trials No.	Outcome	Trials No.	Outcome	Trials No.	Outcome	Trials No.	Outcome	Trials No.	Outcome
Response rate^a
RR (95% CI)	52	0.50 (0.45, 0.55)	31	0.43 (0.38, 0.49)	21	0.62 (0.55, 0.69)	29	0.60 (0.54, 0.65)	23	0.39 (0.32, 0.48)
I² (%)		96		95		94		93		97
Progression-free survival
HR (95% CI)	10	0.39 (0.28, 0.53)	9	0.37 (0.26, 0.54)	1	0.53 (0.41, 0.69)	6	0.28 (0.20, 0.41)	4	0.60 (0.41, 0.86)
I² (%)		89		89		NA		80		87
Improvement, median (IQR), mo	7	4.60 (3.80, 8.30)	6	4.60 (3.70, 8.63)	1	4.60 (4.60, 4.60)	4	4.55 (3.50, 8.53)	3	4.60 (3.90, 8.30)
Overall survival
HR (95% CI)	8	0.67 (0.61, 0.73)	5	0.67 (0.59, 0.76)	3	0.67 (0.58, 0.78)	3	0.63 (0.54, 0.73)	5	0.69 (0.61, 0.79)
I² (%)		0		0		0		0		0
Improvement, median (IQR), mo^b	6	4.10 (3.70, 5.60)	3	4.20 (4.00, 5.10)	3	3.70 (3.70, 5.10)	1	4.40 (4.40, 4.40)	4	4.10 (3.78, 6.38)

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NDA, new drug application; BLA, biologics license application; HR, hazard ratio; mo, month; NA, not available.

The response rate was derived from single-arm trials.

Immature overall survival data for 2 pivotal clinical trials.

Fig. 3 — **Forest plot of response rate from the single-arm trials for cancer drugs.** One investigator-initiated study (dinutuximab beta) was not evaluated due to the lack of a specified primary efficacy endpoint. The block and whiskers represent the weighted average and 95% CI. The diamond represents the pooled estimates, respectively. P represents the P value for heterogeneity. A P value <0.10 was considered significant heterogeneity. RR, response rate; CI, confidence interval.

Additionally, we performed a pooled analysis of the RR reported by no less than two pivotal trials for drug classes or cancer types (eTable S4). The magnitude of RR varied significantly across the different drug classes (ranging from 36% to 85%) and cancer types (ranging from 14% to 72%). For drug classes, the highest pooled RR was 85% for BTK inhibitors, followed by EGFR inhibitors (71%) and RET inhibitors (68%). In contrast, the pooled RR was the weakest among HER2 inhibitors (36%) (eTable S4). For cancer types, the highest pooled RR for the treatment of lymphoma was 72%, followed by thyroid cancer (70%) and ovarian cancer (64%) (eTable S4).

The pooled HR for PFS was 0.39 (95% CI: 0.28, 0.53; I² = 89%; Fig. 4) while the HR for OS was 0.67 (95% CI: 0.61, 0.73; I² = 0%; Fig. 5). The median PFS and OS of CA drugs were 7.6 and 14.8 months, respectively. The pooled HR for PFS was much lower for small-molecule drugs than for biological products (0.28 vs. 0.60, P = 0.0049) while no significant difference was found between solid and hematologic cancers (0.37 vs. 0.53, P = 0.123) (Table 3). Additionally, there was no difference in HR of OS in relation to cancer types and drug classes (Table 3). The median PFS and OS were improved by 4.6 months and 4.1 months, respectively, compared to the control group (Table 3).

Sensitivity analysis showed that the pooled RR, PFS and OS were reliable after omitting each study. The pooled OS (P = 0.194 for Egger’s test; P = 0.138 for Begg’s test) and PFS (P = 0.075 for Egger’s test; P = 0.089 for Begg’s test) were assessed without publication bias. However, the pooling RR from single-arm trials had a publication bias (P < 0.0001 for Egger’s test; P < 0.0001 for Begg’s test).

Safety of CA cancer drugs

For the single-arm trials, the pooled treatment-related SAE and Grade ≥3 AEs were 15% (95% CI: 0.11–0.19; I² = 87%) and 25% (95% CI: 0.20–0.31; I² = 94%), respectively. For the RCT, the pooled analysis of treatment-related SAE (RR = 1.65, 95% CI: 0.97–2.81, P = 0.065) and Grade ≥3 AEs (RR = 1.11, 95% CI: 0.52–2.34, P = 0.793) showed no significant difference between CA cancer drugs and their respective controls. Sensitivity analysis showed that the pooled treatment-related Grade ≥3 AEs were reliable after omitting each study. However, significant changes were observed in the pooled results of treatment-related SAE (RR = 2.00, 95% CI: 1.31–3.03, P = 0.0012) after omitting pembrolizumab for the treatment of colorectal cancer.⁴³ The pooled results of treatment-related SAE and Grade ≥3 AEs derived from RCT showed no publication bias. In contrast, there was publication bias if sourced from a pooled single-arm trial design.

Correlation between surrogate endpoints and OS

As of April 1, 2023, we reviewed the evidence for 59 indications involving the surrogate endpoint validation vs. OS was reported in the literature as level-1 evidence (eTable S5). The scatter plots of trial-level association between HR of time-to-event surrogate and HR of OS based on the published studies are shown in eFigures S2–S4. Of the 72 CA indications granted in China, eight indications had OS (including the coprimary of PFS and OS) as the primary endpoint and one indication was supported by the investigator-initiated clinical trial without the specified primary efficacy endpoint (Table 1 and eTable S2). Therefore, we evaluated the correlation between surrogate endpoints and OS in the remaining 63 indications by using the literature summary (eTable S6). No evidence of correlation was reported in 73% (46/63) of indications between surrogate endpoints and OS. Three indications reported surrogate endpoints that were highly related to OS (r ≥ 0.85). The others included two indications that were moderately correlated (0.70 < r < 0.85) and 12 indications that were lowly correlated (r ≤ 0.70). Additionally, a significant difference in the correlation strength of surrogate endpoints vs. OS was observed between the domestic and imported CA drugs (eTable S7). Subgroup analysis showed that the strength of correlation between imported and domestic CA drugs was comparable in the single-arm trial design and the RCT trial design (eTable S8).

Discussion

This study aimed to provide a comprehensive assessment of the pre-approval clinical trial for the cancer indications received CA in China since the implementation of the expedited program. Our results showed an increasing trend in the number of CA indications used in China in recent years, reflecting the NMPA's regulatory flexibility for serious and life-threatening cancers without available therapy. Among these 72 CA indications, 41 indications are developed by domestic developers and the majority of them (39/41) are only available in China (except for two indications of zanubrutinib that were also approved by the FDA or EMA). Furthermore, 31 imported cancer indications were approved via CA programs, which might greatly shorten the development time of these drugs in China. Our previous study confirmed that cancer drugs granted CA significantly reduced the delay of imported cancer drugs from 48 months to 24 months.¹⁴ These findings supported the importance of CA implementation to address the availability of cancer drugs for cancer patients in China.

Although the generic design of CA (AA or CMA) programs may be similar in different countries or regions (eTable S9), there are minor regulatory differences between them.¹⁰^,⁴⁴ We analyzed the approval status of CA cancer indications (33 indications) granted in China at the FDA and EMA. Of these, our results showed that the FDA approved 32 indications, of which 41% (13/32) and 59% (19/32) were regular approval and AA in the initial approval, respectively. Correspondingly, the EMA also approved 30 cancer indications with a smaller percentage of drugs granted the CMA than that of the FDA (40% vs. 59%). This may be due partially to the fact that the EMA only grants initial indications for CMA, while the FDA and NMPA allowed initial and supplemental indications for drugs with AA and CA.⁷^,¹⁰ The majority of cancer drugs were approved in China much later compared to these drugs that were also approved by the FDA and EMA. We further analyzed the pivotal clinical trials of these drugs and noted a lack of Chinese population trial data for most cancer indications (25/33). This may explain why some cancer indications have received regular approval in the FDA or EMA but granted CA in China. Additionally, for the 31 imported cancer indications approved in China, 30 were approved by the FDA (except the dinutuximab beta) and 28 by the EMA (except the ivosidenib, pralatrexate and pralsetinib). These data demonstrated the consistency between the FDA and EMA in the benefit-risk assessment of most cancer drugs. Once cancer drugs granted CA/AA/CMA would require post-marketing confirmatory studies. As China's participation in the synchronized global R&D system deepens, it is expected that encouraging Chinese populations to participate in MRCT and establishing the similar regulatory requirement with the FDA and EMA will help accelerate the completion of confirmatory clinical trials, which can reduce the uncertainty of this expedited program.

Significant differences between domestic and imported drugs in receiving CA were observed, including the choice of primary efficacy endpoint, clinical trial design, lines of therapy and cancer sites. Compared to the RCT design for domestic drugs, the RCT design for imported drugs were all MRCT design, which may require a larger sample size for inclusion. It should be admitted that the CA program in China is still in a preliminary stage of development compared to the AA and CMA programs of the FDA and EMA. China has long been recognized for its significant delays in introducing cancer drugs to the market despite that there have been notable improvements attributed to drug regulatory reforms recently. Our preliminary study showed that only 58 were approved for marketing in China compared to the 123 new molecular entities approved by FDA for cancers between 2010 and 2021.¹⁴ In an attempt to expedite the approval of novel cancer drugs already marketed abroad with significant clinical value, these drugs may be prioritized for accelerated approval using a CA program in China due to the lack of data from Chinese population trials. Our study further confirmed that only 20% of imported drugs (6/31) included the Chinese population trial data, while all domestic cancer drugs (41/41) were primarily focused on the Chinese population. These findings can be also supported by the benefit-risk assessment of the CA drug review report issued by the NMPA (data not shown). Therefore, the lack of Chinese populations may be the primary reason for the significant difference in regulatory considerations for obtaining CA for domestic and imported anticancer drugs. With the implementation of ICH E17 guidelines in 2019 in China, the imported drugs are encouraged to conduct MRCT which include the Chinese population. Therefore, it can be expected that the regulatory considerations of CA may not be significantly different for domestic and imported drugs in the coming years.¹⁴ However, it should be acknowledged that the sample size included in this study was limited and the difference of CA granted for domestic and imported drugs needs to be further validated.

Although the use of surrogate endpoints rather than OS as an outcome can accelerate the launch of cancer drugs, whether this translates into clear patient benefit remains controversial.⁴⁵ The FDA reported a total of 93 new cancer indications granted AA since the initiation of this expedited program until May 31, 2017. Of these, AA based on the RR (including hematologic response rates) accounted for up to 86%.⁵ It should be admitted that some of these drugs granted AA using surrogate endpoints were withdrawn from the market after failing to demonstrate improved OS in the confirmatory clinical trials. For example, durvalumab was granted AA by the FDA for the treatment of uroepithelial carcinoma based on RR, but it did not improve OS and even worsened PFS in confirmatory clinical trial.⁴⁶ In this study, we noted that the majority of cancer indications obtained CA in China were also for RR (75%) similar to that of the FDA. A previous systematic review analysis indicated that RR generally correlated poorly with OS in most cancers.³⁷ Another study reported that 80% of cancer drugs that failed in post-approval trials were initially granted AA based on RR by the FDA, emphasizing that RR may provide a poor surrogate for overall survival.⁴⁴ Therefore, establishing a correlation between surrogate endpoints and clinical endpoints (e.g., survival or quality of life) has significant implications for improving the likelihood of patient benefit.⁴⁷

The present study found the significant differences in RR across cancer types and drug classes, suggesting that there are challenges in determining the appropriate RR values for CA. The highest pooled RR was up to 72% for lymphoma, while the lowest RR was only 14% for liver cancer. It was observed that patients with hematologic cancers had higher RR compared to patients with solid cancers. Regarding the different drug classes, our study showed 85% of RR for BTK inhibitors while only 36% was for HER2 inhibitors in solid cancers. However, it may not be reasonable to interpret that higher values of RR are associated with longer OS or better quality of life. As in the case of antibodies against programmed cell death 1 (PD1), a strong improvement in overall survival has been observed in the absence of a large effect on the RR.⁴⁸ Therefore, to evaluate the treatment effects of correlations between the surrogate endpoints and OS in specific cancer types for different drug classes may be warranted.

Notably, the use of validated surrogate endpoints is still relatively limited, especially those with a high correlation with survival. The previous study showed that 39 (61%) were found to have no documented the correlation between surrogate endpoint with OS among 64 initially approved cancer agents based on surrogates in the US. Moreover, only 3 (5%) of surrogates had a high correlation with OS (r ≥ 0.85).²² Another study reported that 56% of cancer indications obtained AA by the FDA did not have any reported correlation analysis between surrogate endpoints and OS.³⁷ Similarly, it was reported that the correlation between the majority of the surrogate endpoints and OS was inconclusive among 141 cancer indications approved in China.³¹ In this study, we analyzed the treatment effect of correlation between surrogate endpoints and OS via a systematic review, suggesting that 73% of the surrogate endpoints had no documented validation with OS, and only 3% of the surrogate endpoints were highly correlated with OS (r ≥ 0.85), similar to the previous studies.²²^,³¹ The use of unvalidated surrogate endpoints may expose patients to an ineffective treatment setting and even be detrimental to patient clinical benefit. Hence, establishing a correlation between surrogate endpoints and OS will contribute to the success of CA programs.

The safety of cancer drugs is another significant concern within the CA program. However, considering the reliance on single-arm trials for the majority of CA cancer indications, there is limited availability of safety data. To our knowledge, few studies have evaluated the safety of CA or other similar programs (e.g., AA of the FDA and CMA of the EMA) for cancer indications. In this study, we attempted to pool treatment-related adverse events from pivotal clinical trials for CA cancer indications. Treatment-related SAE (15%) and Grade ≥3 AEs (25%), pooled by single-arm trials, suggested a tolerable safety risk for these CA cancer agents. However, the lack of a parallel control group in single-arm trials makes it difficult to determine whether the adverse event was caused by the disease or the drug itself.⁴⁹ Additionally, we pooled treatment-related SAE and Grade ≥3 AEs from RCT, indicating that there was no significant safety concern observed for CA cancer drugs in comparison to the control group. Nevertheless, it is important to acknowledge that these findings are based on limited sample size, and thus, the safety concerns highlighted should be interpreted with caution. To obtain a more comprehensive evaluation, it is essential to monitor these safety concerns in a larger sample size in future studies.

Given that cancer drugs granted CA were primarily based on single-arm trials and surrogate endpoints, post-marketing confirmatory clinical trials are an extremely significant component of ensuring that these drugs can actually translate into clinical benefit for patients. However, in recent years, FDA’s AA program has been widely criticized due to laxity in post-marketing confirmatory clinical studies.²⁴^,⁵⁰ Gyawali BD et al. evaluated 93 cancer indications that received an AA from the FDA and showed that only one-fifth used OS as the primary endpoints in confirmatory clinical trials, while the remaining used the same or different surrogate endpoints as compared with pre-approval trials.³⁴ The continued use of unvalidated surrogate endpoints in confirmatory clinical trials also raises concerns about clinical benefit. In addition, cancer drugs granted AA did not complete confirmatory clinical trials in the timely fashion have also received contested. A study performed by the U.S. Department of Health and Human Services showed that between 1992 and 2022, 34% of drug applications granted AA had confirmatory trials completed later than the pre-specified completion date, even including four drugs that exceeded the originally specified time by five years.⁵¹ The prolonged use of cancer drugs without proven clinical benefit may expose patients to high costs and ineffective treatments.⁵²^,⁵³ In another study, it was reported that for cancer drugs that initiated confirmatory clinical trials at the time of AA, the median time to complete confirmatory clinical trials and then move to regular approval was 3 years, significantly less than the 5.1 years to initiate confirmatory clinical trials after AA.⁵⁴ Similarly, this finding was also confirmed for non-oncology drugs that received AA.⁵⁵ To address these controversies, in March 2023, the FDA issued new draft industry guidance entitled “Clinical trial considerations to support accelerated approval of oncology therapeutics” promising to address the use of surrogate endpoints and confirmatory clinical trial completion.⁵⁶

Compared to the United States, China is still in the preliminary stages of CA program implementation. This study did not analyze whether these CA oncology agents translate into clinical benefits in confirmatory clinical trials due to the lack of available data. Based on the FDA's experience with the AA program, the China NMPA realized some potential challenges of the CA program. In the latest published guidance (March 2023), titled "Technical guidance on the suitability of single-arm clinical trials to support marketing applications for cancer drugs", it was emphasized that confirmatory clinical trials should be initiated for cancer drugs prior to obtaining a CA.⁵⁷ Moreover, China specifies strict timelines for the completion of confirmatory clinical trials in CA cancer drugs. The validity of the drug registration certificate for CA drugs is marked with 5 years and the required time for the completion of the confirmatory clinical trial. Considering the 200-day review timeframe for supplement applications of confirmatory clinical trials, the data should be submitted within 4 years of the CA.²⁰ As the increasing numbers of CA cancer drugs are approved for marketing, China should further clarify the requirements for confirmatory clinical trial evidence, including the use of clinical endpoints and RCT designs as much as feasible. In addition, it is recommended that China should develop specific procedures to ensure that these cancer drugs can be withdrawn or revoked from the market in the first instance when no clinical benefit is demonstrated.

In summary, the implementation of the CA program in China has significant implications for addressing the availability of drugs for many cancers characterized by a complex prognosis and suboptimal treatment options in clinical practice. However, as in other countries, the CA program implemented in China also faces the challenges of uncertainty about clinical benefit due to the extensive use of surrogate endpoints with limited validation evidence. As the increasing number of cancer indications granted by CA, our results highlighted that China should introduce the OS/quality of life as much as feasible in the confirmatory clinical trials to ensure the clinical benefit to patients.

This study had some limitations. First, we used meta-analysis for the primary endpoints (e.g., PFS and RR) across indications, which may have led to higher heterogeneity, although subgroup analysis and sensitivity analysis was conducted. Therefore, the results of meta-analyses with significant heterogeneity should be interpreted with caution. Second, this study assessed the treatment effects of correlation at the trial level between surrogate endpoints and OS by published systematic reviews and meta-analysis. However, validation of surrogate endpoints may also be performed within the NMPA, which may lead to bias in our results. Similar to the previous study, we graded the correlation strength of surrogate endpoints according to the Institute of Quality and Efficiency in Health Care, which may not be consistent with the NMPA interpretation.²⁸ Thereby, this result may vary depending on the criteria assessed.

Third, for the pooled analysis of this study, we selected either a random-effects model or a fixed-effects model relying mainly on the magnitude of I². Therefore, we used a random effects model to assess the overall RR and HR of PFS due to significant heterogeneity. However, it should be admitted that random-effects weights are based on the summation of within-study and between-studies variances, not only the within-study variance. It is possible that small trials were biased toward reporting an effect and possibly had lower quality. Consequently, the small trials included in this study were potentially biased to report effects and may have been of lower quality, although sensitivity analyses were performed. In addition, this study focused on evidence of primary efficacy endpoints and safety in pivotal clinical trials for CA cancer drugs. However, secondary endpoints including duration of response and complete response rates are also essential data to support the CA cancer indication. It should be admitted that when regulatory agencies make benefit-risk assessments and decisions, they take into account various factors beyond just the ORR or PFS results. Other evidence, including manufacturing quality, convenience, and availability of the drug in the country, should be also considered in future studies.

Fourth, this study assessed the correlation between surrogate endpoints and OS of CA cancer drugs based on similar cancer indications and primary efficacy endpoints without consideration of drug class, mainly because many studies did not identify specific drug classes (e.g., pooled the results of chemotherapy, targeted drugs and immunotherapy) in the correlation studies. This may lead to biased assessment results. In light of this, it is imperative for future studies to include drug classes to comprehensively explore the correlation between surrogate endpoints and OS. Finally, the pooling of RR, treatment-related SAE, and Grade ≥3 AEs was derived from a single-arm trial with possible publication bias. This raised the possibility that some studies with small samples of negative results were not submitted or not accepted for publication.

Contributors

XXL contributed to study design, data interpretation, data analysis and drafted the manuscript. XD contributed to study design, data collection, data analysis and data interpretation. LH contributed to data collection, data analysis and data interpretation. QXG contributed to data analysis, data interpretation and figure production. XFL contributed to data collection, data analysis and data interpretation. MW contributed to data analysis and data interpretation. HPL contributed to data analysis and data interpretation. YZ contributed to data analysis and data interpretation. XCX contributed to data analysis and data interpretation. ZQL contributed to data analysis and data interpretation. JWL contributed to data analysis and data interpretation. SCC contributed to study design, data interpretation and data analysis. YY contributed to study design, data interpretation and data analysis. All authors were involved in each stage of the preparation and revision of the manuscript. XXL, XD and YY have accessed and verified the underlying data. YY and SCC were responsible for the decision to submit the manuscript. All authors read and approved the final version of the manuscript.

Data sharing statement

The relevant original source documents are cited in full in the reference section and supplement. The raw data can be shared with others on reasonable request via email to the corresponding author of this manuscript.

Declaration of interests

We declare no competing interests.

Acknowledgements

We thank the funder of Tsinghua-Peking Joint Centers for Life Sciences (CLS).

Footnotes

Translation: For the language translation of the abstract see Supplementary Materials section.

^{Appendix A}

Supplementary data related to this article can be found at https://doi.org/10.1016/j.eclinm.2023.102177.

Contributor Information

Xingxian Luo, Email: luoxingxian@tsinghua.edu.cn.

Xin Du, Email: duxin9705@163.com.

Shein-Chung Chow, Email: sheinchung.chow@duke.edu.

Yue Yang, Email: yanghappy@tsinghua.edu.cn.

Appendix A. Supplementary data

Supplementary Appendix

mmc1.docx^{(1.4MB, docx)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Appendix

mmc1.docx^{(1.4MB, docx)}

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PERMALINK

Evidence of pre-approval clinical trial supporting the granted conditional approval for novel cancer drugs in China between 2015 and 2022

Xingxian Luo

Xin Du

Lin Huang

Qixiang Guo

Xufeng Lv

Cen Wang

Haopeng Liu

Yue Zhou

Xuecai Xue

Zhuangqi Li

Jingwen Liu

Shein-Chung Chow

Yue Yang

Summary

Background

Methods

Findings

Interpretation

Funding

Research in context.

Evidence before this study

Added value of this study

Implications of all the available evidence

Introduction

Fig. 1.

Methods

Data sources

Data extraction

Ethics

Identification of pivotal trials

Correlation between surrogate endpoints and OS

Statistics

Role of the funding source

Results

Characteristics of CA cancer drugs and pivotal trials

Table 1.

Fig. 2.

Table 2.

Efficacy of CA cancer drugs

Table 3.

Fig. 3.

Fig. 4.

Fig. 5.

Safety of CA cancer drugs

Correlation between surrogate endpoints and OS

Discussion

Contributors

Data sharing statement

Declaration of interests

Acknowledgements

Footnotes

Contributor Information

Appendix A. Supplementary data

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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