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, and arrary data analysis.

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 “What Is a Stepped-Wedge Cluster Randomized Trial?” has been accepted by JAMA Internal Medicine.

[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.

Grant

NIH NIAID K99/R00 AI173395 Improving the design and statistical analysis of cluster-randomized trials on tropical infectious diseases Role: PI

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.

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.