Emulating the LEADER trial in China: a regulatory science case study on non-interventional research

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Authors: Jun Zhao, Xiaona Xin, Yuanyuan Song, George Zhang, Ying Wu, Jun Wang, Fuqiang Cui
Cited by: 0
References: 22

Abstract

Introduction: Integrating real-world evidence (RWE) into regulatory decision-making requires validation against pivotal randomized controlled trials for the same estimand. We emulated the LEADER trial using Chinese claims data to evaluate liraglutide's cardiovascular safety, assessing RWE-RCT concordance and examining the methodological adaptations and operational challenges encountered when emulating RCTs with claims data. Materials and methods: Using the Beijing Municipal Medical Insurance Database (2020-2024), we emulated the LEADER protocol. We identified new users of liraglutide and, as an active comparator, new users of DPP-4 inhibitors (DPP4i). The primary outcome was 3-point Major Adverse Cardiovascular Events. Propensity score matching was employed to balance baseline characteristics. Hazard Ratios were estimated using Cox proportional hazards models. Negative control outcomes (fractures and head injuries) and E-values were employed to assess the potential for unmeasured confounding. Results: = 0.063), consistent with the effect size observed in the LEADER trial (HR 0.87), though not reaching statistical significance. However, sensitivity analysis adjusting for baseline comorbidities and drug exposure demonstrated a significant benefit (HR 0.90; 95% CI: 0.80, 1.01). The negative control analysis showed no association (HR 1.02; 95% CI: 0.93, 1.12), supporting the absence of unmeasured confounding. Conclusion: This target trial emulation study validated the cardiovascular safety profile of liraglutide observed in the LEADER trial. Liraglutide demonstrated non-inferiority to DPP-4 inhibitors for the risk of MACE. While the primary analysis for superiority showed a trend toward benefit, it did not reach statistical significance. However, sensitivity analyses suggested potential benefits and supporting the robustness of the safety findings.

Fields of Study & Subjects

Advanced Causal Inference Techniques Diabetes Treatment and Management Statistical Methods in Clinical Trials

Publication details

Journal / source
Frontiers in Endocrinology
Volume
17
Pages
1777954–1777954
Publication date
12 March 2026
Language
EN