Discrepant effect estimates in randomized clinical trials between high-income and low/middle-income countries: A meta-research study
Year: 2025
Type: article
Study designs: Systematic Review, Meta-Analysis
Abstract: BACKGROUND: Randomized clinical trials (RCTs) in low/middle-income countries (LMICs) frequently produce evidence inconsistent with that conducted in high-income countries (HICs). This study aimed to compare RCT effect estimates in LMICs and HICs. METHODS: We identified RCTs from meta-analyses in six leading general medical journals and the Cochrane Database of Systematic Reviews between 2018 and 2023. Only RCTs that recruited participants after 2006 and used a negative control were included. Within each meta-analysis, RCTs sponsored by and enrolling participants in LMICs were designated as the exposed group, while those sponsored by and enrolling participants in HICs were the control group. Effect estimates were transformed into odds ratios (ORs); within each meta-analysis, ORs from LMICs and HICs were combined separately and compared as a ratio of ORs (RORs). RORs were combined across meta-analyses and subgrouped by patient-reported, investigator-assessed, and hard outcomes (e.g., mortality). An ROR larger than 1 indicated a larger OR from LMICs than from HICs. FINDINGS: 1,005 RCTs (423 from LMICs and 582 from HICs) were identified from 140 meta-analyses. The overall ROR was 1.73 (95% confidence interval: 1.44-2.08). The RORs were 1.94 (1.45-2.61), 1.78 (1.35-2.35), and 1.04 (0.81-1.34) for patient-reported, investigator-assessed, and hard outcomes, respectively. The ROR decreased to 1.04 (0.87-1.24) when restricted to RCTs with a low risk of bias. CONCLUSIONS: Our study suggested that RCTs from LMICs may produce larger effect estimates than those from HICs, with the difference substantially smaller for RCTs with a low risk of bias, indicating that bias may contribute to the discrepancy. FUNDING: This work was supported by the Shenzhen Science and Technology Program, Shenzhen Institutes of Advanced Technology, and China Postdoctoral Science Foundation.
Source: Med
Authors: Yuanxi Jia, Peng Wu, Yiwen Jiang, Yuanyuan Yao, Yujun Long, Jianzhao Liu, Shiyu Wang, Zhirong Yang, Wei Wang, Wei Wang, Yang Wang, Wenyao Wang, Wenyao Wang, Hao Wu, Karen A. Robinson, Jinling Tang
Institutions: National University of Singapore, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Lanzhou University, Southern University of Science and Technology, University of Cambridge, Shenzhen University of Advanced Technology, Capital Medical University, Beijing Anzhen Hospital, Peking University Third Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Fu Wai Hospital, Johns Hopkins University, Chinese University of Hong Kong
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $9,350
Funders: Chinese Academy of Sciences, China Postdoctoral Science Foundation, Science, Technology and Innovation Commission of Shenzhen Municipality, Shenzhen Municipal Science and Technology Innovation Council, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Science and Technology Innovation Program
Awards: G3287006345, G4649948783, G5830732070
OpenAlex ID: W4415435142