Wei Ma
 |
Associate Professor |
Bio
Wei Ma received his B.S. degree in Mathematics from Zhejiang University in 2009 and his Ph.D. degree in Statistics from the University of Virginia in 2014. After his Ph.D., he worked in the pharmaceutical industry as a Biostatistician with Biogen for three years. In 2017, he returned to academia as an Assistant Professor in the Institute of Statistics and Big Data at Renmin University of China. He became an Associate Professor in 2021 and was promoted to tenure in 2023.
Research Interests
Adaptive Design
Biostatistics and Bioinformatics
Design and Analysis of Clinical Trials
Health Big Data Analytics
Professional Activities and Awards
Elected Member, International Statistical Institute, 2024.
Associate Editor, Statistica Sinica, 2024–2026.
Selected Publications
Ye, X., Hu, F., and Ma, W. (2024) Robustness of response-adaptive randomization. Biometrics, 80(2), ujae049.
Ma, W., Li, P., Zhang, L. X., and Hu, F. (2024) A new and unified family of covariate adaptive randomization procedures and their properties. Journal of the American Statistical Association, 119(545), 151–162.
Gu, Y., Liu, H., and Ma, W. (2023) Regression-based multiple treatment effect estimation under covariate-adaptive randomization. Biometrics, 79(4), 2869–2880.
Liu, H., Tu, F., and Ma, W. (2023) Lasso-adjusted treatment effect estimation under covariate-adaptive randomization. Biometrika, 110(2), 431–447.
Ma, W., Tu, F., and Liu, H. (2022). Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective. Statistics in Medicine, 41(29), 5645–5661.
Wang, T. and Ma, W. (2021) The impact of misclassification on covariate-adaptive randomized clinical trials. Biometrics, 77(2), 451-464.
Ma, W., Qin, Y., Li, Y., and Hu, F. (2020) Statistical inference for covariate-adaptive randomization procedures. Journal of the American Statistical Association, 115(531), 1488-1497.
Ma, W., Hu, F., and Zhang, L. X. (2015) Testing hypotheses of covariate-adaptive randomized clinical trials. Journal of the American Statistical Association, 110(510), 669-680.