Methods

Corresponding author; † Student/Supervisee
Open-access

• Mao, L. (2025). Win ratio for partially ordered data. Statistica Sinica, 10.5705/ss.202023.0321. [R-package poset]
• Mao, L. (2024). Robust improvement of efficiency using information on covariate distribution. Electronic Journal of Statistics, 18, 4640-4666.
• Mao, L. (2024). Defining estimand for the win ratio: separate the true effect from censoring. Clinical Trials, 21, 584-594. [Presentation]
• Cui, Y., Huang, B., Mao, L., Uno, H., Wei, L.-J., and Tian, L. (2024). Inferences for the distribution of the duration of response in a comparative clinical study. Clinical Trials, 21, 541-552.
• Mao, L. (2024). Wilcoxon–Mann–Whitney statistics in randomized trials with non-compliance. Electronic Journal of Statistics, 18, 465-489. [An earlier version of this manuscript won the 2019 IMS New Researcher Travel Award]
• Mao, L. and Wang, T.† (2024). Dissecting the restricted mean time in favor of treatment. Journal of Biopharmaceutical Statistics, 34, 111-126.
• Wang, T.†, Mao, L., Cocco, A., and Kim, K. (2024). Statistical inference for time-to-event data in non-randomized cohorts with selective attrition. Statistics in Medicine, 43, 216-232.
• Mao, L. (2023). Study design for restricted mean time analysis of recurrent events and death. Biometrics, 79, 3701-3714. [R-package rmt]
• Mao, L. (2023). Nonparametric inference of general while-alive estimands for recurrent events. Biometrics, 79, 1749-1760. [Presentation; R-package WA]
• Mao, L. (2023). Power and sample size calculations for the restricted mean time analysis of prioritized composite endpoints. Statistics in Biopharmaceutical Research, 15, 540-548. [R-package rmt]
• Mao, L. (2023). On restricted mean time in favor of treatment. Biometrics, 79, 61-72. [Presentation; R-package rmt]
• Mao, L. (2022). Nonparametric inference of complier quantile treatment effects in randomized trials with imperfect compliance. Biostatistics & Epidemiology, 6, 249-265.
• He, Y., Kim S., Mao, L., and Ahn, K.W. (2022). Marginal semiparametric transformation models for clustered multivariate competing risks data. Statistics in Medicine, 41, 5349-5364.
• Wang, T.† and Mao, L. (2022). Stratified proportional win-fractions regression analysis. Statistics in Medicine, 41, 5305-5318. [R-package WR]
• Mao, L. , Kim, K., and Miao, X.† (2022). Sample size formula for general win ratio analysis. Biometrics, 78, 1257-1268. [R-package WR]
• Mao, L. (2022). Identification of the outcome distribution and sensitivity analysis under weak confounder-instrument interaction. Statistics & Probability Letters, 189, 109590.
• Mao, L. (2022). On the relative efficiency of intent-to-treat Wilcoxon–Mann–Whitney test in the presence of non-compliance. Biometrika, 109, 873-880.
• Mao, L. , Kim, K., and Li, Y.† (2022). On recurrent-event win ratio. Statistical Methods in Medical Research, 31, 1120-1134. [Presentation; R-package WR]
• Mao, L. and Wang, T.† (2021). A class of proportional win-fractions regression models for composite outcomes. Biometrics, 77, 1265-1275. [Presentation; R-package WR]
• Mao, L. and Kim, K. (2021). Statistical models for composite endpoints of death and non-fatal events: a review. Statistics in Biopharmaceutical Research, 13, 260-269.
• Li, Y.†, Liang, M., Mao, L. , and Wang, S. (2021). Robust estimation and variable selection for the accelerated failure time model. Statistics in Medicine, 40, 4473-4491.
• Mao, L. (2020). A unified approach to the calculation of information operators in semiparametric models. Biometrika, 107, 983-995.
• Dong, G., Mao, L., Huang, B., Gamalo-Siebers, M., Wang, J., Yu, G., and Hoaglin, D. (2020). The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring. Journal of Biopharmaceutical Statistics, 30, 882-899.
• Mao, L. (2019). Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies. Statistics in Medicine, 38, 3628-3641.
• Mao, L. (2019). Nonparametric inference on tumor incidence with partially identified cause-of-death data. In: L. Zhang et al. (eds) Contemporary Biostatistics with Biopharmaceutical Applications, pp 3-18, Springer.
• Mao, L. (2019). Nonparametric identification and estimation of current status data in the presence of death. Statistica Neerlandica, 73, 395-413.
• 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. [Presentation]
• Mao, L., Lin, D.Y., and Zeng, D. (2017). Semiparametric 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 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.