Chenxi Li, Ph.D.

Assistant Professor of Biostatistics
Division of Biostatistics
PhD in Statistics, University of Wisconsin-Madison

Michigan State University
Department of Epidemiology and Biostatistics
909 Wilson Road Room B601
Michigan State University
East Lansing 48824
517.353.8623
cli@epi.msu.edu

Research Interests

Statistical analysis of interval-censored failure time data and multivariate survival analysis including competing risks analysis, frailty models and multi-state models as well as their applications to biomedical sciences.

Selected Publications

Li, C., Pak, D. and Todem, D. Adaptive Lasso for the Cox Regression with Interval Censored and Possibly Left Truncated Data. Statistical Methods in Medical Research, in press.

Pak, D., Li, C. and Todem, D. Semiparametric Analysis of Correlated and Interval-Censored Event-History Data. Statistical Methods in Medical Research, in press.

Li, C. Doubly Robust Weighted Log-Rank Tests and Renyi-Type Tests Under Non-random Treatment Assignment and Dependent Censoring. Statistical Methods in Medical Research, in press.

Li, C. (2018). Two-Sample Tests for Survival Data from Observational Studies. Lifetime Data Analysis, 24, 509-531.

Li, C. (2016). Cause-Specific Hazard Regression for Competing Risks Data Under Interval Censoring and Left Truncation. Computational Statistics and Data Analysis, 104, 197-208.

Pak, D., Li, C., Todem, D. and Sohn, W. (2017). A Multi-State Model for Correlated Interval Censored Life History Data in Caries Research. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66, 413-423

Li, C. (2016). The Fine-Gray Model Under Interval Censored Competing Risks Data. Journal of Multivariate Analysis, 143, 327-344.

Li, C., Dowling, N. M. and Chappell, R. (2015). Quantile Regression with a Change-point Model for Longitudinal Data: An Application to the Study of Cognitive Changes in Preclinical Alzheimer's Disease. Biometrics, 71, 625-635.

Hudgens, M., Li, C. and Fine, J. (2014). Parametric likelihood inference for interval censored competing risks data. Biometrics, 70, 1-9.

Li, C. and Fine, J. (2013). Smoothed Nonparametric Estimation for Current Status Competing Risks Data. Biometrika, 100, 173-187.

Li, C., Wei, Y., Chappell, R. and He, X. (2011). Bent Line Quantile Regression with Application to an Allometric Study of Land Mammals' Speed and Mass. Biometrics, 67, 242-249.