Adjunct Professor of Biostatistics
Yi Li’s current research interests are in developing methodologies for analyzing high dimensional correlated discrete and continuous outcome data and correlated censored failure time data. These types of data arise frequently from many fields of biomedical research, such as from population studies, survey sampling, clinical trials and genomic studies.
His specific statistical interests lie in nonparametric maximum likelihood estimation, semiparametric estimating equations, large sample approximations, marginalized frailty survival models, measurement error problems, cure modeling, spatial models, adaptive designs and hidden Markov models with applications in genomics. His methodologic research is funded by a sustained NIH R01 grant starting from year 2003.
Yi Li is actively involved in collaborative research in clinical trials and observational studies with researchers from Dana-Farber Cancer Institute, Harvard Medical School and Harvard School of Public Health. The applications have included cancer trials, cancer preventive studies, racial disparities in cancer cures, and cancer genomics.
Ph.D., 1999, University of Michigan
Li, Y., Betensky, R., Louis, D. and Cairncross, J. (2002) The use of frailty hazard models for unrecognized heterogeneity that interacts with treatment: considerations of efficiency and power. Biometrics, 58(1):232-236.
Li, Y. and Ryan, L. (2002) Modeling Spatial Survival Data Using The Semi-parametric Frailty Models. Biometrics, 58(2): 287-297.
Li, Y. and Lin, X. (2003) Functional inference in frailty measurement error models for clustered survival data using the SIMEX approach. Journal of the American Statistical Association, 98(461):191-204.
Li, Y., Ryan, L., Bellamy, S. and Satten, G. (2003) Inference on clustered survival data using imputed frailties. Journal of Computational and Graphical Statistics, 12(3):640-662.
Li, Y. and Lin, X. (2003) Testing random effects in uncensored/Censored clustered Data with categorical responses. Biometrics, vol 59, 1, 25-35.
Li, Y. and Ryan, L. (2004) Survival analysis with heterogeneous measurement error. Journal of the American Statistical Association, 99(467), 724-735.
Li, Y. and Feng, J. (2005) A Nonparametric Comparison of Conditional Distributions with Nonnegligible Cure Fractions. Lifetime Data Analysis, 11, 367-387. [PDF]
Li, Y. and Lin, X. (2006) Semiparametric Normal Transformation Models for Spatially Correlated Survival Data. Journal of the American Statistical Association, 101, 591-603.
Li, Y. and Ryan, L. (2006) Inference on survival data with covariate measurement error – an imputation approach. Scandinavian Journal of Statistics, 33(2), 169-190.
Li, Y., Shih, M. and Betensky, R. (2007) Designed Extension of Survival Studies: Application to Clinical Trials with Unrecognized Heterogeneity. Statistica Sinica, 17, 1567-1589.
Li, Y., Tiwari, R. and Guha, S. (2007) Mixture Cure Survival Models with Dependent Censoring. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 285-306.
Li, Y. and Tiwari, R. (2008) Comparing Trends in Cancer Rates Across Overlapping Regions. Biometrics, 64, 1280-1286.
Li, Y., Tang, H. and Lin, X. (2008) Covariate Measurement Errors in Spatial Linear Mixed Models. Statistica Sinica, in press.
Li, Y., Tiwari, R. and Zou, Z. (2008) An Age-Stratified Poisson Model for Comparing Trends in Cancer Rates Across Overlapping Regions. Biometrical Journal, 50, 608-619.
Li, Y., Prentice, R. and Lin, X. (2008) Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data. Biometrika, 95, 947-960.