Evidence map›Paper›PMID 42618456›Full record

ArticleCancer medicine2026

Design Characteristics and Survival Outcomes of Local Trials Supporting the Approval of Targeted Anticancer Drugs in China.

Leyuan Qi, Xiaoyan Chang, Zixuan Zhuang, Yafang Huang

Abstract read
In one paragraph

Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Leyuan QiSixth Clinical School, Capital Medical University, Beijing, China.
Xiaoyan ChangSchool of Basic Medical Sciences, Capital Medical University, Beijing, China.
Zixuan ZhuangSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Yafang HuangSchool of General Practice and Continuing Education, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0003-4396-3870

Funding

National Natural Science Foundation of China 82104133National Natural Science Foundation of China 82574373
6 · The paper itself

Abstract

backgroundChina has rapidly advanced in precision oncology, driven by numerous targeted anticancer drugs approved locally. However, characteristics of design and outcomes of pivotal trials that enrolled Chinese patients and were conducted locally to support these approvals remain unknown.

methodWe identified all targeted anticancer drugs approved in China before 31 December 2022, and collected the therapeutic outcomes of the trials involving Chinese patients, based on publicly available data and regulatory comments.

resultAmong 300 oncology indications for 112 targeted anticancer drugs approved in China, 201 (67.0%) were supported by randomized controlled trials (RCTs). Of 201 indications, 36 (17.9%) indications were supported by 36 local RCTs showing outcomes from the Chinese population. Across these RCTs, 22 (61.1%) had a double-blind design and 19 (52.8%) used a placebo as control. Drugs' HRs were 0.68 (95% CI 0.63-0.73, I

conclusionTargeted anticancer drugs significantly reduced the risk of death and cancer progression in Chinese populations, although OS gain was modest. However, the local trials involving Chinese patients still need to be increased. This study revealed characteristics of local trial evidence, facilitating treatment decision-making for Chinese physicians and patients and supporting sponsors in planning and designing local trials.

Indexed as

Antineoplastic AgentsDrug ApprovalMolecular Targeted TherapyNeoplasmsResearch DesignChinaHumansRandomized Controlled Trials as TopicTreatment OutcomeAntineoplastic AgentsChinese participantsrandomized controlled trialstargeted anticancer drugstherapeutic outcometrial design

Identifiers

PMID42618456
PMCPMC13489815

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.