Biography

I am a tenure-track assistant professor in the Department of Biostatistics, School of Public Health, University of Michigan. My research interests are in causal inference, randomized clinical trials, clustered data and their interactions with infectious disease research.

I was a postdoctoral researcher in the Statistics and Data Science Department of the Wharton School, University of Pennsylvania (advised by Dylan Small and Nicholas P. Jewell). I got my PhD degree from the Biostatistics department at Johns Hopkins Bloomberg School of Public Health (advised by Michael Rosenblum and Brian Caffo).

My CV is available here.

I wrote blogs on my job search experience in 2022-2023 here and my K99/R00 application experience here.

[News] A manuscript “How to achieve model-robust inference in stepped wedge trials with model-based methods?” has been accepted by Biometrics.

[News] A manuscript “On the robustness and precision of mixed-model analysis of covariance in cluster-randomized trials” has been accepted by Statistical Science.

[News] I am fortunate to receive the 2024 IMS New Researcher Travel Award.

[News] A manuscript “Model-robust and efficient inference for cluster-randomized experiments” has been accepted by Journal of American Statistical Association: Theory and Methods.

[News] A manuscript “Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Covariate Adjustment” has been cited by the FDA in their 2023 Guidance for Industry: “Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biologics.”

Causal inference for randomized clincial trials

• Bingkai Wang, Michael O. Harhay, Dylan S. Small, Tim P. Morris, and Fan Li. (2024). “On the robustness and precision of mixed-model analysis of covariance in cluster-randomized trials.” Statistical Science.

• Bingkai Wang and Fan Li. (2024) “Asymptotic inference with flexible covariate adjustment under rerandomization and stratified rerandomization.” arXiv:2406.02834.

• Bingkai Wang, Xueqi Wang, and Fan Li. (2024) “How to achieve model-robust inference in stepped wedge trials with model-based methods?” Biometrics.

• Bingkai Wang, Fan Li, and Rui Wang. (2024) “Handling incomplete outcomes and covariates in cluster-randomized trials: doubly-robust estimation, efficiency considerations, and sensitivity analysis.” arXiv: 2401.11278.

• Bingkai Wang, Fan Li, and Mengxin Yu. (2024) “Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations.” arXiv: 2401.01977.

• Bingkai Wang, Chan Park, Dylan Small, and Fan Li. (2023). “Model-robust and efficient inference for cluster-randomized experiments.” Journal of the American Statistical Association, Theory and Methods Section.

• Bingkai Wang, Yu Du. (2021). “Robustly leveraging the post-randomization information to improve precision in the analyses of randomized clinical trials.” International Journal of Biostatistics.

• Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and Michael Rosenblum. (2021). “Model-robust inference for clinical trials that improve precision by stratified randomization and covariate adjustment.” Journal of the American Statistical Association, Theory and Methods Section, 118(542): 1152-1163.

• Bingkai Wang, Elizabeth L. Ogburn, and Michael Rosenblum. (2019). “Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions.” Biometrics, 75(4): 1391-1400.

Statistical methods for test-negative designs in infectious disease research

• Mengxin Yu, Kendrick Qijun Li, Nicholas Jewell, Eric Tchetgen Tchetgen, Dylan Small, Xu Shi, Bingkai Wang. (2023) “Test-negative designs with various reasons for testing: statistical bias and solution .” arXiv.
• Bingkai Wang, Suzanne M. Dufault, Dylan S. Small, Nicholas P. Jewell. (2022). “Randomization Inference for Cluster-Randomized Test-Negative Designs with Application to Dengue Studies: Unbiased estimation, Partial compliance, and Stepped-wedge design.” Annals of Applied Statistics, 17(2): 1592-1614.

Statistical methods for brain imaging

• Bingkai Wang, Brian S. Caffo, Xi Luo, Chin-Fu Liu, Andreia V. Faria, Michael I. Miller, and Yi Zhao. (2022) “Regularized regression on compositional trees with application to MRI analysis.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(3): 541- 561.
• Bingkai Wang, Xi Luo, Yi Zhao, and Brian Caffo. (2021) “Semiparametric partial common principal component analysis for covariance matrices.” Biometrics, 77(4): 1175-1186.

Awards

• IMS New Researcher Travel Award, 2024.
• Election to membership of the Phi Beta Kappa Society (honor for excellence in scholarship), 2021.
• Best student paper runner-up, ASA Biopharmaceutical Section, 2021.
• Margaret Merrell Award (awarded to one doctoral student per year for outstanding research), Johns Hopkins University Department of Biostatistics, 2021.
• Distinguished student paper award, ENAR International Biometric Society, 2021.
• Student paper award, the Statistical Meeting in Imaging, 2020.
• Center of Excellence in Regulatory Science and Innovation (CERSI) Scholarship, U.S. Food and Drug Administration and Johns Hopkins University, 2017-2021.
• Shanghai outstanding undergraduate student (for top 1% senior-year undergraduate students), 2016.
• Fudan University undergraduate research fellowship, 2015-2016.
• National Scholarship (for top 1% undergraduate students in China per year), 2014-2015.
• Shanghai Scholarship (for top 5% undergraduate students in Shanghai), 2013.

Invited Commentary Articles

• Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, Michael Rosenblum. (2021). Comment: Inference after covariate-adaptive randomisation: aspects of methodology and theory. Statistical Theory and Related Fields.
• Michael Rosenblum and Bingkai Wang. (2019) “The Critical Role of Statistical Analyses in Maximizing Power Gains From Covariate-Adaptive Trial Designs.” JAMA network open 2(4): e190789-e190789.

Invited talks

• Model-robust and efficient inference for cluster-randomized experiments, Society for Clinical Trials Annual Meeting, May 2023.
• Randomization Inference for Cluster-Randomized Test-Negative Designs with Application to Dengue Studies, Scientific meeting of the World Mosquito Program, February 2022.
• Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Additional Baseline Variables.
  • ICSA Applied Statistics Symposium, September 2021
  • Novartis Statistics Seminar, September 2021
  • JSM, August 2021
  • Johns Hopkins University Biostatistics Departmental Seminar, September 2020
  • ENAR, March 2020
• Semiparametric Partial Common Principal Component Analysis for Covariance Matrices. SMI, May 2020.