Guanqing Chen
Papers
3
Total Citations
10
H-Index
2
About
Guanqing Chen is a rising scholar in biostatistics and health services research, whose work focuses on the intersection of social network analysis and hierarchical modeling to understand how peer effects shape healthcare outcomes. His primary research areas include Bayesian hierarchical network autocorrelation models, the diffusion of medical technologies, and the estimation of direct and indirect peer influences among hospitals. Chen’s major contributions lie in developing novel statistical frameworks that embed network autocorrelation within hierarchical data structures, enabling researchers to disentangle how hospitals influence each other’s quality of care and adoption of innovations—even when patient-level data is nested within institutions. His most cited paper, “Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients” (2024, 5 citations), introduces a groundbreaking approach to modeling contagion in healthcare settings. Chen has also pioneered four families of network autocorrelation models for binary outcomes, addressing a critical gap in social network analysis. With a growing citation record and a clear focus on translating complex statistical methods into actionable insights for healthcare policy, Chen is establishing himself as a key contributor to the field of network-based health outcomes research.
Research Focus
Key Achievements
Top Papers
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