Chenxi Li, Ph.D.

Assistant Professor of Biostatistics
Division of Biostatistics
PhD in Statistics, University of Wisconsin-Madison 2010

Michigan State University
Department of Epidemiology and Biostatistics
909 Wilson Road Room B601
Michigan State University
East Lansing 48824

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

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.