Research

We develop methodological advances in meta-analysis and evidence synthesis to support large-scale comparative effectiveness research and safety monitoring. Our work addresses multivariate and network meta-analysis, publication bias, small-study effects, and dynamic monitoring of adverse events. These methods enable patient-centered treatment ranking, real-time pharmacovigilance, and more reliable aggregation of evidence across heterogeneous studies and data sources.

Selected papers:

• Duan, R., Tong, J., Lin, L., Levine, L., Sammel, M., Stoddard, J., … & Chen, Y. (2023). Palm: Patient-centered treatment ranking via large-scale multivariate network meta-analysis. The annals of applied statistics, 17(1), 815. Annual Best Paper Award.
• Huang, J., Cai, Y., Du, J., Li, R., Ellenberg, S. S., Hennessy, S., … & Chen, Y. (2021). Monitoring vaccine safety by studying temporal variation of adverse events using vaccine adverse event reporting system. The Annals of Applied Statistics, 15(1), 252-269. Annual Best Paper Award.
• Hong, C., Salanti, G., Morton, S. C., Riley, R. D., Chu, H., Kimmel, S. E., & Chen, Y. (2020). Testing small study effects in multivariate meta-analysis. Biometrics, 76(4), 1240-1250.
• Marks‐Anglin, A., & Chen, Y. (2020). A historical review of publication bias. Research synthesis methods, 11(6), 725-742.
• Lin, L., Chu, H., Murad, M. H., Hong, C., Qu, Z., Cole, S. R., & Chen, Y. (2018). Empirical comparison of publication bias tests in meta-analysis. Journal of general internal medicine, 33(8), 1260-1267.