lmao [at] biostat.wisc.edu
BS 2005 Tsinghua University, China
MS 2010 Drexel University
PhD 2016 University of North Carolina at Chapel Hill
Causal inference, missing or censored data, semiparametric theory, clinical trials, statistical learning in medical studies, diagnostic radiology, cardiovascular disease.
IMS New Researcher Travel Award, 2019
Larry Kupper Doctoral Dissertation Publication Award, UNC Chapel Hill, 2017
ENAR RAB Poster Award, 2016
ASA Biometrics Section Travel Award, 2016
ASA Biopharmaceutical Section Student Paper Award, 2015
ENAR Distinguished Student Paper Award, 2015
Delta Omega Award for Academic Excellence, UNC Chapel Hill, 2012
Special Commendations for PhD Qualifying Exams, UNC Chapel Hill, 2011
Mao, L. (2019). Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies. Statistics in Medicine, 10.1002/sim.8201.
Colgan, T. J., Van Pay, A. J., Sharma, S. D., Mao, L., and Reeder, S. B. (2019). Diurnal variation of proton density fat fraction in the liver using quantitative chemical shift encoded MRI. Journal of Magnetic Resonance Imaging,10.1002/jmri.26814.
Mao, L. (2019). Nonparametric identification and estimation of current status data in the presence of death. Statistica Neerlandica, 10.1111/stan.12175.
Harringa, J.B., Bracken, R.L., Davis, J.C., Mao, L., Kitchin, D.R., Robbins, J.B., Ziemlewicz, T.J., Pickhardt, P.J., Reeder, S.B., and Repplinger, M.D. (2019). Prospective evaluation of MRI compared with CT for the etiology of abdominal pain in emergency department patients with concern for appendicitis. Journal of Magnetic Resonance Imaging,10.1002/jmri.26728.
Longo, K., Knott, E., Watson, R., Sweitlik, J., Vlaisavljevich, E., Smolock, A. R., Xu, Z., Cho, C., Mao, L., Lee, F. T., and Ziemlewicz, T. (2019). Robotically assisted sonic therapy (RAST) for hepatic ablation in a porcine model: Mitigation of body wall damage with an optimized pulse sequence. CadioVascular and Interventional Radiology, 42, 1016–1023.
Mao, L. (2019). On the alternative hypotheses for the win ratio. Biometrics, 75, 347-351.
Mao, L. (2018). On causal estimation using U-statistics. Biometrika, 105, 215-220.
Mao, L., Lin, D.Y. and Zeng, D. (2017). Regression analysis of interval-censored competing risks data. Biometrics, 73, 857–865. [An earlier version of this manuscript won the 2016 ASA Biometrics Section Travel Award and the 2016 ENAR RAB Poster Award]
Mao, L. and Lin, D.Y. (2017). Efficient estimation in semiparametric transformation models for the cumulative incidence of competing risks. Journal of the Royal Statistical Society, Series B, 79, 573–587. [An earlier version of this manuscript won the 2015 ENAR Distinguished Student Paper Award]
Zeng, D, Mao, L., and Lin, D.Y. (2016). Maximum likelihood estimation for semiparametric transformation models with interval-censored data. Biometrika, 103, 253-271.
Mao, L. and Lin, D.Y. (2016). Semiparametric proportional rate regression for the weighted composite endpoint of recurrent and terminal events. Biostatistics, 17, 390-403.
[An earlier version of this manuscript won the 2015 ASA Biopharmaceutical Section Student Paper Award]
Gallo, P., Mao, L., and Shih, V.H. (2014). Alternative views on setting clinical trial futility criteria. Journal of Biopharmaceutical Statistics, 24, 976-993.
Causal inference, missing or censored data, semiparametric theory, clinical trials, statistical learning in medical studies, diagnostic medicine, cardiovascular disease.