Jim Anthony
David Barondess
Ahnalee Brincks
  Gustavo de los Campos
Honglei Chen
Debra Furr-Holden
Joseph Gardiner
Hector M. González
Kelly Hirko
Claudia Holzman
Carol Janney
  Allan Kozlowski
Jean Kerver
Chenxi Li
Qing Lu
Zhehui Luo
Claire Margerison-Zilko
Janet Osuch
Nigel Paneth
Dorothy Pathak
James Pivarnik
Mat Reeves
A.Mahdi. Saeed
Nicole Talge
David Todem
Ana Vázquez
Elizabeth (Betsy) Wasilevich
Lixin Zhang
  Adjunct Faculty
  Emeritus Faculty


  David Barondess 


  Madeleine Lenski


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 Fee 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

Li, C. Cause-Specific Hazard Regression for Competing Risks Data Under Interval Censoring and Left Truncation. Computational Statistics and Data Analysis, in press.

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

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.