data science
inference on high-dimensional regression
survival analysis with high-dimensional covariates
variable screening and selection
statistical models for high-dimensional/correlated (clustered, longitudinal, spatial) data
measurement error problems
random effects (frailty) models for unobserved heterogeneity
adaptive design in clinical trials
cure modeling for early stage cancers
hidden markov model and its application in genomics
National Cancer Institute, PI: Yi Li 5T32CA009337, 2009-2011 Biostatistics/Epidemiology Training Grant in Biostatistics (Harvard)
National Cancer Institute, PI: Yi Li 5T32CA009337, 2011-2016 Biostatistics/Epidemiology Training Grant in Biostatistics (Harvard)
National Cancer Institute, PI. Yi Li R01 CA95747, 2003-2007 Community-based Studies in Cancer and Environment
National Cancer Institute, PI. Yi Li R01 CA95747, 2008-2012 Community-based Studies in Cancer and Environment
National Cancer Institute, PI. Yi Li 1P01CA134294-010002, 2008-2013 New Statistical Methods for Cancer Surveillance
National Cancer Institute, PI. Yi Li R21CA157219, 2011-2013 Integrated Statistical Analysis for Cancer Genomic Studies
National Institute on Aging, co-PI. Yi Li R21AG058198, 2018-2021 Functional Regression and Subgroup Analysis for Asynchronous Longitudinal Data
National Cancer Institute, PI. Yi Li R01CA249096, 2021-2025 New Statistical Methods for Modelling Cancer Outcomes
National Cancer Institute, PI. Yi Li R01CA269398, 2022-2026 Detecting Racial Disparities in Cancer Survival by Integrating Multiple High-dimensional Observational Studies
Regular Member: NIH BMRD study section, 2008-2012
Panel of Review: Mathematical Reviews, 2005-
Referee: Annals of Statistics, Journal of the American Statistical Association (Theory and Methodology; Applications and Case Studies), Journal of the Royal Statistical Society B (Statistical Methodology), Biometrika, Statistica Sinica, Biometrics, Biometrical Journal, Statistics in Medicine, Journal of Computational and Graphical Statistics, Biostatistics, Lifetime Data Analysis, Annals of the Institute of Statistical Mathematics, Statistics and Probability Letters, Mathematical and Computer Modelling, Journal of Statistical Computation and Simulation, Journal of Computational Statistics and Data Analysis, Journal of Statistical Planning and Inference, TEST, Communications in Statistics, Geomatica, Journal of Nonparametric Statistics, European Series in Applied and Industrial Mathematics (ESAIM): Probability and Statistics, Journal of Ultrasound in Medicine, Journal of Clinical Oncology
111. Sun, Y., Salerno, S., Pan, Z., Yang, E., Sujimongkol, C., Song, J., Wang, X., Han, P., Zeng, D., Kang, J., Christiani, D., and Li, Y. (2024) Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned. Harvard Data Science Review, 6.1. Video Highlights 110. Sun, Y., Kang, J., Haridas, C., Mayne, N., Potter, A., Yang, C., Christiani, D. and Li, Y. (2024) Penalized deep partially linear Cox models with application to CT scans of lung cancer patients. Biometrics, in press. Supplementary Material
111. Sun, Y., Salerno, S., Pan, Z., Yang, E., Sujimongkol, C., Song, J., Wang, X., Han, P., Zeng, D., Kang, J., Christiani, D., and Li, Y. (2024) Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned. Harvard Data Science Review, 6.1.
110. Sun, Y., Kang, J., Haridas, C., Mayne, N., Potter, A., Yang, C., Christiani, D. and Li, Y. (2024) Penalized deep partially linear Cox models with application to CT scans of lung cancer patients. Biometrics, in press.
109. Rong, Y., Zhao, D., Zheng, X. and Li, Y. (2023) Kernel Cox partially linear regression: building predictive models for cancer patients' survival. Statistics in Medicine, in press.
108. Song, J., Guha, S. and Li, Y. (2023) Bayesian inference for high dimensional Cox models with Gaussian and Diffused-Gamma priors: a case study of mortality in COVID-19 patients admitted to the ICU. Statistics in Biosciences, in press. Supplementary Material
108. Song, J., Guha, S. and Li, Y. (2023) Bayesian inference for high dimensional Cox models with Gaussian and Diffused-Gamma priors: a case study of mortality in COVID-19 patients admitted to the ICU. Statistics in Biosciences, in press.
107. Xia, L., Nan, B. and Li, Y. (2023) De-biased lasso for stratified Cox models with application to the national kidney transplant data. Annals of Applied Statistics, in press. Supplementary Material
107. Xia, L., Nan, B. and Li, Y. (2023) De-biased lasso for stratified Cox models with application to the national kidney transplant data. Annals of Applied Statistics, in press.
106. Yang, Y., Pan, Z., Kang, J., Brummett, C. and Li, Y. (2023) Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach. Biometrics, in press. Supplementary Material
106. Yang, Y., Pan, Z., Kang, J., Brummett, C. and Li, Y. (2023) Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach. Biometrics, in press.
105. Sun, Y., Salerno, S., He, X., Pan, Z., Yang, E., Sujimongkol, C., Song, J., Wang, X., Han, P., Kang, J., Sjoding, M., Jolly, S., Christiani, D., and Li, Y. (2023) Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Scientific Reports-Nature, 13, 7318. Supplementary Material 104. Lin, H., Liu, L., Liu, J. and Li, Y. (2023) A combined moment equation approach for spatial autoregressive models. Canadian Journal of Statistics, in press. DOI: 10.1002/cjs.11784. 103. Chang, X., Li, YH. and Li, Y. (2023) Asynchronous and error-prone longitudinal data analysis via functional calibration. Biometrics, in press. Supplementary Material
105. Sun, Y., Salerno, S., He, X., Pan, Z., Yang, E., Sujimongkol, C., Song, J., Wang, X., Han, P., Kang, J., Sjoding, M., Jolly, S., Christiani, D., and Li, Y. (2023) Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Scientific Reports-Nature, 13, 7318.
104. Lin, H., Liu, L., Liu, J. and Li, Y. (2023) A combined moment equation approach for spatial autoregressive models. Canadian Journal of Statistics, in press. DOI: 10.1002/cjs.11784.
103. Chang, X., Li, YH. and Li, Y. (2023) Asynchronous and error-prone longitudinal data analysis via functional calibration. Biometrics, in press.
102. Liu, W., Lin, H., Liu, L., Ma, Y., Wei, Y. and Li, Y. (2023) Supervised structural learning of semiparametric regression on high-dimensional correlated covariates with applications to eQTL studies. Statistics in Medicine, in press. Supplementary Material 101. Sun, Y., Kang, J., Brummett, C. and Li, Y. (2023) Individualized risk assessment of preoperative opioid use by interpretable neural network regression. Annals of Applied Statistics, 17, 434-453. Supplementary Material
102. Liu, W., Lin, H., Liu, L., Ma, Y., Wei, Y. and Li, Y. (2023) Supervised structural learning of semiparametric regression on high-dimensional correlated covariates with applications to eQTL studies. Statistics in Medicine, in press.
101. Sun, Y., Kang, J., Brummett, C. and Li, Y. (2023) Individualized risk assessment of preoperative opioid use by interpretable neural network regression. Annals of Applied Statistics, 17, 434-453.
100. Yang, Y., Kang, J. and Li, Y. (2022) A soft-thresholding operator for sparse time-varying effects in survival models. In Zhao, Y. eds. Precision Medicine: Methods and Applications, Springer, in press. An R package on GitHub 99. Zhang, C., Lin, H., Liu, L., Liu, J. and Li, Y. (2023) Functional data analysis with covariate-dependent mean and covariance structures. Biometrics, 79(3), 2232-45. Supplementary Material 98. Salerno, S. and Li, Y. (2023) High-dimensional survival analysis: methods and applications. Annual Review of Statistics and Its Application, 10, 25-49. 97. Xia, L., Nan, B. and Li, Y. (2023) Statistical inference for Cox proportional hazards models with a diverging number of covariates. Scandinavian Journal of Statistics, 50(2), 550-71.
100. Yang, Y., Kang, J. and Li, Y. (2022) A soft-thresholding operator for sparse time-varying effects in survival models. In Zhao, Y. eds. Precision Medicine: Methods and Applications, Springer, in press.
99. Zhang, C., Lin, H., Liu, L., Liu, J. and Li, Y. (2023) Functional data analysis with covariate-dependent mean and covariance structures. Biometrics, 79(3), 2232-45.
98. Salerno, S. and Li, Y. (2023) High-dimensional survival analysis: methods and applications. Annual Review of Statistics and Its Application, 10, 25-49.
97. Xia, L., Nan, B. and Li, Y. (2023) Statistical inference for Cox proportional hazards models with a diverging number of covariates. Scandinavian Journal of Statistics, 50(2), 550-71.
96. Zhang, E. and Li, Y. (2023) High dimensional Gaussian graphical regression models with covariates. Journal of the American Statistical Association, 118(543), 2088-2100. https://doi.org/10.1080/01621459.2022.2034632 Code 95. Xia, L., Nan, B. and Li, Y. (2023) De-biased lasso for generalized linear models with a diverging number of covariates. Biometrics, 79(1), 344-357. doi:10.1111/biom.13587
96. Zhang, E. and Li, Y. (2023) High dimensional Gaussian graphical regression models with covariates. Journal of the American Statistical Association, 118(543), 2088-2100. https://doi.org/10.1080/01621459.2022.2034632
95. Xia, L., Nan, B. and Li, Y. (2023) De-biased lasso for generalized linear models with a diverging number of covariates. Biometrics, 79(1), 344-357. doi:10.1111/biom.13587
94. Jiang, J., Lin, H., Peng, H., Fan, G. and Li, Y. (2022) Cluster analysis with regression of non-Gaussian functional data on covariates. Canadian Journal of Statistics, 50, 221-40. 93. Jiang, J., Lin, H., Zhong, Q. and Li, Y. (2022) Analysis of multivariate non-Gaussian functional data: a semiparametric latent process approach. Journal of Multivariate Analysis, 189, 104888. https://doi.org/10.1016/j.jmva.2021.104888 92. Fei, Z., Zheng, Q., Hong, H. and Li, Y. (2023) Inference for high dimensional censored quantile regression. Journal of the American Statistical Association, 18(542), 898-912. doi:10.1080/01621459.2021.1957900
94. Jiang, J., Lin, H., Peng, H., Fan, G. and Li, Y. (2022) Cluster analysis with regression of non-Gaussian functional data on covariates. Canadian Journal of Statistics, 50, 221-40.
93. Jiang, J., Lin, H., Zhong, Q. and Li, Y. (2022) Analysis of multivariate non-Gaussian functional data: a semiparametric latent process approach. Journal of Multivariate Analysis, 189, 104888. https://doi.org/10.1016/j.jmva.2021.104888
92. Fei, Z., Zheng, Q., Hong, H. and Li, Y. (2023) Inference for high dimensional censored quantile regression. Journal of the American Statistical Association, 18(542), 898-912. doi:10.1080/01621459.2021.1957900
91. Fei, Z. and Li, Y. (2021) Estimation and inference for high dimensional generalized linear models: a splitting and smoothing approach. Journal of Machine Learning Research, 22, 1-32.
90. He, Z., Kang, J., Zhu, J. and Li, Y. (2022) Stratified Cox models with time-varying effects for national kidney transplant patients: a new block-wise steepest ascent method. Biometrics, 78, 1221-32.
89. Zhong, Q., Lin, H. and Li, Y. (2021) Cluster non-Gaussian functional data. Biometrics, 77(3), 852-65.
Supplementary Material
88. Lin, H., Liu, J., Li, H., Pan, L. and Li, Y. (2022) Efficient estimation and computation in generalized varying coefficient models with unknown link and variance functions for large-scale data. Stat Sinica, 32, 847-68. 87. Pijyan, A., Zheng, Q., Hong, H. and Li, Y. (2020) Consistent estimation of generalized linear models with high dimensional predictors via stepwise regression. Entropy, 22(9), 965.
88. Lin, H., Liu, J., Li, H., Pan, L. and Li, Y. (2022) Efficient estimation and computation in generalized varying coefficient models with unknown link and variance functions for large-scale data. Stat Sinica, 32, 847-68.
87. Pijyan, A., Zheng, Q., Hong, H. and Li, Y. (2020) Consistent estimation of generalized linear models with high dimensional predictors via stepwise regression. Entropy, 22(9), 965.
86. Hong, H. and Li, Y. (2020) Estimation of time-varying reproduction numbers underlying epidemiological processes: a new statistical tool for the COVID-19 pandemic. PLOS ONE, 15(7):e0236464.
85. Fei, Z., Zhu, J., Banerjee, M. and Li, Y. (2019) Drawing inference on high dimensional linear models: a selection-assisted partial regression and smoothing approach. Biometrics, 75(2), 551-61.
Software
84. Pan, L., Li, YH., He, K., Li, YM. and Li, Y. (2020) Generalized linear mixed models with Gaussian mixture random effects: inference and application. Journal of Multivariate Analysis, 175, 100455.
83. Zheng, Q., Hong, H. and Li, Y. (2020) Building generalized linear models with ultrahigh dimensional features: a sequentially conditional approach. Biometrics, 76, 47-60
82. Morris, E., Li, Y., He, Z., Li, Y. and Kang, J. (2020) SurvBoost: An R package for high-dimensional variable selection in the stratified proportional hazards model via gradient boosting. The R Journal, 12, 105-17.
An R package on CRAN
81. Lin, H., Hong, H., Yang, B., Zhang, Y., Liu, W., Fan, G. and Li, Y. (2019) Nonparametric time-varying coefficient models for panel data. Stat in Biosciences, 11, 548–66.
80. Li, Y., Hong, H. and Li, Y. (2019) Multiclass linear discriminant analysis with ultrahigh dimensional features. Biometrics, 75, 1086-97.
79. Hong, H., Zheng, Q. and Li, Y. (2019) Forward regression for Cox models with high dimensional covariates. Journal of Multivariate Analysis, 173, 268-90.
78. Zucker, D., Zhou, X., Liao, X., Li, Y. and Spiegelman, D. (2019) A modied partial likelihood score method for Cox regression with covariate error under the internal validation design. Biometrics, 75(2), 414-27.
77. Hong, H., Chen, X., Kang, J. and Li, Y. (2020) The Lq-norm learning for ultrahigh-dimensional survival data: an integrative framework. Stat Sinica, 30, 1213-33.
76. Rong, Y., Zhao, D., Zhu, J., Yuan, W., Cheng, W. and Li, Y. (2018) More accurate semiparametric regression in pharmacogenomics. Statistics and Its Interface, 11(4), 573-80.
75. He, K., Jiang, H., Zhou, X., Wen, W. and Li, Y. (2018) False discovery control for penalized variable selections with high-dimensional covariates. Statistical Applications in Genetics and Molecular Biology, 17(6), 20180038.
74. He, K., Kang, J., Hong, H., Zhu, J., Li, Y., Lin, H., Xu, H. and Li, Y. (2019) Covariance insured screening. Computational Statistics and Data Analysis, 132, 100-14.
Software and Manual
73. Wu, F., Kim, S., Qin, J., Saran, R. and Li, Y. (2018) A pairwise likelihood augmented Cox estimator for left-truncated data. Biometrics, 74, 100-8.
Software on CRAN-R
72. Hong, H., Chen, C., Christiani, D. and Li, Y. (2018) Integrated powered density: screening ultrahigh-dimensional covariates with survival outcomes. Biometrics, 74, 421-9.
71. Li, Y., Hong, H., Ahmed, S. and Li, Y. (2019) Weak signals in high-dimensional regression: detection, estimation and prediction. Applied Stochastic Models in Business Industry, 35, 283–98.
70. Marino, M. and Li, Y. (2017) Factor analysis of correlation matrices when the number of random variables exceeds the sample size. Statistical Theory and Related Fields, 1, 246-56.
69. Hong, H. and Li, Y. (2017) Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review. Appl Math Ser B, 32, 379-96.
68. Kang, J., Hong, H. and Li, Y. (2017) Partition based ultrahigh dimensional variable screening. Biometrika, 104, 785-800.
67. Jha, C., Li, Y. and Guha, S. (2017) Semiparametric Bayesian analysis of high-dimensional censored outcome data. Statistical Theory and Related Fields, 1, 194-204.
66. Hong, H., Kang, J. and Li, Y. (2018) Conditional screening for ultra-high dimensional covariates with survival outcomes. Lifetime Data Anal, 24, 45-71.
65. Marino, M., Buxton, O. and Li, Y. (2017) Covariate selection for multilevel models with missing data. Stat, 6, 31-46.
64. Ma, Y., Li, Y., Lin, H. and Li, Y. (2017) Concordance measure-based feature screening and variable selection. Stat Sinica, 27, 1967-85.
Software for (1) variable screening (2) variable selection
63. He, K., Yang, Y., Li, Y., Zhu, J., and Li, Y. (2017) Modeling time-varying effects with large-scale survival data: an efficient quasi-Newton approach. Journal of Computational and Graphical Statistics, 26, 635-45.
62. Lehmann, D., Li, Y., Saran, R. and Li, Y. (2017) Strengthening instrumental variables through weighting. Stat in Biosciences, 320-38.
61. He, K., Li, YM., Wei, Q. and Li, Y. (2017) Computationally efficient approach for modeling complex and big survival data. In: Ahmed, S. eds. Big and Complex Data Analysis: Statistical Methodologies and Applications, Springer, 193-207.
60. Lin, H., Fei, Z. and Li, Y. (2016) A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment. Scandinavian Journal of Statistics, 43, 649-63.
R Code
59. He, K., Li, Y., Zhu, J., Liu, H., Lee, J., Amos, C., Hyslop, T., Jin, J., Lin, H., Wei, Q. and Li, Y. (2016) Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates. Bioinformatics, 32, 50-7.
Software Manual
58. Zhao, L., Shi, J., Shearon, T. and Li, Y. (2015) A Dirichlet process mixture model for survival outcome data: assessing nationwide kidney transplant centers. Stat in Med, 34, 1404-16.
57. Xu, P., Zhu, J., Zhu, L. and Li, Y. (2015) Covariance enhanced discriminant analysis. Biometrika, 102, 33-45.
56. Zheng, Y., Fei, Z., Zhang, W., Baccarelli, A., Li, Y. and Hou, L. (2014) PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures. Bioinformatics, 30, 2802-2807.
55. Zhao, D. and Li, Y. (2014) Score test variable screening. Biometrics, 70, 862-71.
54. Lin, H., Zhou, L., Song, X. and Li, Y. (2014) Semiparametric transformation models for semicompeting survival data. Biometrics, 70, 599-607.
53. Zhou, L., Lin, H., Song, X., and Li, Y. (2014) Selection of latent variables for multiple mixed-outcome models. Scandinavian Journal of Statistics, 41, 1064-82.
52. Xu, P., Zhu, L. and Li, Y. (2014) Ultrahigh dimensional time course feature selection. Biometrics, 70, 356-65.
51. Goodman, M., Li, Y. Stoddard, A., and Sorensen, G. (2014) Analysis of ordinal outcomes with longitudinal covariates subject to missingness. Journal of Applied Statistics, 41, 1040-52.
50. He, Z., Kalbfleisch, J., Li, J., and Li, Y. (2013) Evaluating hospital readmission rates in dialysis facilities by adjusting for hospital effects. Lifetime Data Analysis, 19, 490-512.
49. Lin, H., Zhou, L., Elashoff, R., and Li, Y. (2014) Semiparametric latent variable transformation models for multiple mixed outcomes. Stat Sinica, 24, 833-54.
48. Li, Y., Dicker, L. and Zhao, D. (2014) The Dantzig selector for censored linear regression models. Stat Sinica, 24, 251-68.
47. Zucker, D., Gorfine, M., Li, Y., Tadesse, M. and Spiegelman, D. (2013) A regularization corrected score method for nonlinear regression models with covariate error. Biometrics, 9, 80-90.
46. Li, Y. (2013) Book review of "Modeling Survival Data Using Frailty Models" by David D. Hanagal. Journal of the American Statistical Association, 108, 1136-6.
45. Kim, S., Zeng, D., Li, Y. and Spiegelman, D. (2013) Joint modeling of longitudinal and cure-survival data. Journal of Statistical Theory and Practice, 7, 324-44.
44. Kim, S., Zeng, D., Chambless, L. and Li, Y. (2012) Joint models of longitudinal data and recurrent events with informative terminal event. Statistics in Biosciences, 4, 262-81.
43. Wang, H., Zhou, J. and Li, Y. (2013) Variable selection for censored quantile regression. Stat Sinica, 23, 145-67.
42. Zhao, D. and Li, Y. (2012) Principled sure independence screening for Cox models with ultra-high-dimensional covariate. Journal of Multivariate Analysis, 105, 397-411.
41. Cook, A., Gold, D. and Li, Y. (2013) Spatial cluster detection for longitudinal outcomes using administrative regions. Communications in Statistics - Theory and Methods, 42, 2105-17.
40. Walters, K., Li, Y., Tiwari, R. and Zou, Z. (2010) A weighted-least-squares estimation approach to comparing trends in age-adjusted cancer rates across overlapping regions. Journal of Data Science, 8, 631-44.
39. Zhao, X., Dai, W., Li, Y., and Tian, L. (2011) AUC based biomarker ensemble with an application on gene scores predicting low bone mineral density. Bioinformatics, 27, 3050-55.
38. Martin, N. and Li, Y. (2011) A new class of minimum power divergence estimators with applications to cancer surveillance. Journal of Multivariate Analysis, 102, 1175-93.
37. Goodman, M., Li, Y., and Tiwari, R. (2011) Detecting multiple change points in piecewise constant hazard functions. Journal of Applied Statistics, 38, 2523-32.
36. Li, Y., Tian, L. and Wei, LJ. (2011) Estimating subject-specific dependent competing risk profile with censored event time observations. Biometrics, 67, 427-35.
Supplementary Material Software
35. Liao, X., Zucker, D., Li, Y. and Spiegelman, D (2011) Survival analysis with error-prone time-varying covariates: a risk set calibration approach. Biometrics, 67, 50-58.
34. Cook, A., Li, Y., Arterburn, D. and Tiwari, R. (2010) Spatial cluster detection for weighted outcomes using cumulative geographic residuals. Biometrics, 66, 783-92.
33. Cook, A., Gold, D. and Li, Y. (2009) Spatial cluster detection for repeatedly measured outcomes while accounting for residential history. Biometrical Journal, 51, 801-18.
32. Engler, D. and Li, Y. (2009) Survival analysis with high-dimensional covariates: an application in microarray studies. Statistical Applications in Genetics and Molecular Biology, 8, Iss. 1, Article 14. DOI: 10.2202/1544-6115.1423
Software for Elastic Net AFT
Software for Elastic Net Cox
31. Othus, M., Li, Y. and Tiwari, R. (2009) A class of semiparametric mixture cure survival models with dependent censoring. Journal of the American Statistical Association, 104, 1241-50.
30. Najita, J., Li, Y. and Catalano, P. (2009) A novel application of a bivariate regression model for binary and continuous outcomes to studies of fetal toxicity. Journal of the Royal Statistical Society -Series C, 58, 555-73.
29. Zhao, D. and Li, Y. (2009) A note on optimal weights and variable selections for multivariate survival data. Science in China (Series A: Mathematics), 52, 1131-33.
28. Li, Y. (2009) Modeling and analysis of spatially correlated data. New Developments in Biostatistics and Bioinformatics (Edited by Fan, JQ et al.), World Scientific, 73-99.
27. Li, Y., Prentice, R. and Lin, X. (2008) Semiparametric maximum likelihood estimation in normal transformation models for bivariate survival data. Biometrika, 95, 947-60.
Technical Report: Asymptotic Properties of Semiparametric Maximum Likelihood Estimator in Normal Transformation Models for Bivariate Survival Data.
26. Li, Y., Tiwari, R. and Zou, J. (2008) An age-stratified Poisson model for comparing trends in cancer rates across overlapping regions. Biometrical Journal, 50, 608-19.
25. Cook, A., Li, Y. (2008) Notes on permutation tests for spatial cluster detection with censored outcome data. Biometrics, 64, 1289-92.
24. Li, Y. and Tiwari, R. (2008) Comparing Trends in cancer rates across overlapping regions. Biometrics, 2008; 64, 1280-6.
23. Li, Y., Tang, H. and Lin, X. (2008) Spatial Linear mixed models with covariate measurement errors. Stat Sinica, 19, 1077-93.
22. 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.
Technical Report: Proofs for ``Mixture Cure Survival Models with Dependent Censoring".
R Package
21. Li, Y. (2007) Discussion of "Semiparametric regression models with censored data" by Zeng and Lin. Journal of the Royal Statistical Society:Series B (Statistical Methodology), 69, 552-4.
20. Li, Y. and Lin, X. (2006) Semiparametric normal transformation models for spatially correlated survival data. Journal of the American Statistical Association, 101, 591-603.
19. Guha, S., Li, Y., Neuberg, D. (2008) Bayesian hidden Markov modeling of array CGH data. Journal of the American Statistical Association, 103, 485-97.
18. Cook, A., Gold, D. and Li, Y. (2007) Spatial clustering detection for censored outcomes. Biometrics, 63, 540-9.
17. Zhang, B., Li, Y. and Betensky, R. (2006) Effects of unmeasured heterogeneity in the linear transformation model for censored data. Lifetime Data Analysis, 12, 191-203.
16. Li, Y., Shih, M. and Betensky, R. (2007) Designed extension of survival studies: application to clinical trials with unrecognized heterogeneity. Stat Sinica, 17, 1567-89.
15. Goodman, M., Li, Y., Bennett, G., Emmons, K. and Stoddard, S. (2006) An optimal evaluation of multiple behavioral risk factors for cancer in a working class, multi-ethnic population. Journal of Data Science, 4, 291-306.
14. Li, Y. and Ryan, L. (2006) Inference on survival data with covariate measurement error - an imputation approach. Scandinavian Journal of Statistics, 33, 169-90.
13. Li, Y. (2006) Random Effects Models. (peer-reviewed book chapter) In: H. Pham eds. Springer Handbook of Engineering Statistics. Springer-Verlag, London, 687-704.
12. Bellamy, S., Li, Y., Lin, X. and Ryan, L. (2005) Quantifying the bias associated with PQL covariate effects in cluster-randomized trials. Stat Sinica, 15, 1015-32.
11. Li, Y. and Feng, J. (2005) A nonparametric comparison of conditional distributions with nonnegligible cure fractions. Lifetime Data Analysis, 11, 367-87.
R code for computing CVM statistics and p-values
10. Bellamy, S., Li, Y., Ryan, L., Lipsitz, S., Jacobson, M. and Wright, R. (2004) Analysis of clustered and interval censored data from a community-based study in asthma. Statistics in Medicine, 23, 3607-21.
9. Li, Y. and Ryan, L. (2004) Survival analysis with heterogeneous measurement error. Journal of the American Statistical Association, 99, 724-35.
8. Li, Y. and Lin, X. (2003) Testing random effects in uncensored/Censored clustered Data with categorical responses. Biometrics, 59, 25-35.
7. 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, 191-204.
6. Li, Y., Ryan, L., S., Bellamy, and Satten, G. (2003) Inference on clustered survival data using imputed frailties. Journal of Computational and Graphical Statistics, 12, 640-62.
5. 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, 232-6.
4. Li, Y. and Ryan, L. (2002) Modeling spatial survival data using semi-parametric frailty models. Biometrics, 58, 287-97.
3. Gray, R. and Li, Y. (2002) Optimal weights for marginal proportional hazards analysis of clustered failure time data. Lifetime Data Analysis, 8, 4-19.
2. Martin, K.J., Graner, E., Li, Y., Price, L., Kritzman, B., Fournier, M., Rhei, E. and Pardee, A. (2001) High-Sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells. Proceedings of the National Academy of Sciences, 98(5), 2646-51.
1. Li, Y. and Lin, X. (2000) Covariate measurement errors in frailty models for clustered survival data. Biometrika, 87, 849-66.
Selected Health Science Publications
107. Wada, N., Li, Y., Gagne, S., Hino, T., Valtchinov, V., Gay, E., Nishino, M., Hammer, M., Madore, B., Guttmann, C., Ishigami, K., Hunninghake, G., Levy, B., Kaye, K., Christiani, D. and Hatabu, H. (2023) Pulmonary embolism in COVID-19 patients among the periods of ancestral strain, and Alpha, Delta, and Omicron variants. Medicine, 102(48), e36417.
106. Wang, X., White, E., Li, Y., Alladin, J. and Christiani, D. (2023) Serial laboratory biomarkers predict escalations of inpatient care intensity and mortality among hospitalized COVID-19 patients. PLOS ONE, 18(11), e0293842.
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18. Yu, C., Wang, S., Pan, B., Wu, M., Ho, C., Smith, T., Li, Y. and Christiani, D. (2005) No Association between Residential Exposure to Petrochemicals and Brain Tumor Risk. Cancer Epidemiology, Biomarkers and Prevention, 14(12), 3007-3009.
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16. Goguen, L. A., Posner, M. R., Tishler, R. Wirth, L. J., Annino, D. J.MD, Sullivan, C. Li, Y., Haddad, R. (2006) Examining the Need for Neck Dissection In the Era of Chemoradiation Therapy for Advanced Head and Neck Cancer. Archives of Otolaryngology - Head & Neck Surgery, 132, 526-531.
15. Haddad, R.I., Tishler, R., Wirth, L., Norris, C., Goguen, L., Sullivan, C., O'Donnell, L., Li Y, and Posner, M.R. (2006) Rate Of Complete Pathological Responses To Docetaxel/Cisplatin/5-Fluorouracil (TPF) Induction Chemotherapy In Patients With Newly Diagnosed, Locally Advanced Squamous Cell Carcinoma Of The Head And Neck (SCCHN). Archives of Otolaryngology - Head & Neck Surgery, 132, 678-681.
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13. Gilbert, J., Li, Y., Pinto, H., Jennings, T., Kies, M., Silverman, P. and Forastiere, A. (2005) Phase II Trial of Taxol in Salivary Gland Malignancies (E1394): A Trial of the Eastern Cooperative Oncology Group. Head and Neck, 28, 197 - 204.
12. Ly, N., Li, Y., Sredl, D., Perkins, D., Finn, P., Weiss, S. and Gold, D. (2005) Early Life Immune Response to Allergens: Does It Predict Elevated IgE Beyond Infancy? Journal of Clinical Immunology, 25, 314-20.
11. Devlin, P., Kazakin, J., Adak, S., Li, Y., Noris, C., Clark, J. R., Posner, M. R., and Busse, Paul M. (2004) Prospective Phase II Trial of PFL-Induction Chemotherapy Followed by Definitive Local Treatment for Advanced Squamous Cell Carcinoma of the Head and Neck: 10 Year Follow-up. American Journal of Clinical Oncology, 27(4), 369-75.
10. Langer, C., Li, Y., Jennings T., DeConti, C., Nair, S., Cohen R., Forastiere A. (2005) Phase II Evaluation of 96-Hour Paclitaxel Infusion in Advanced (Recurrent or Metastatic) Squamous Cell Carcinoma of the Head and Neck (E3395): A Trial of the Eastern Cooperative Oncology Group, Cancer Investigation, 22, 823-31.
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8. Gibson, M., Li, Y., Murphy, B., Hussain, M. H. DeConti, R. C., Ensley, J., Forastiere, A. A. (2005) A Randomized Phase III Evaluation of Paclitaxel + Cisplatin versus Cisplatin + 5-FU in Advanced Head and Neck Cancer (E1395): An Intergroup Trial of the Eastern Cooperative Oncology Group. Journal of Clinical Oncology, 23, 3562-7.
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5. Adelstein, D., Li, Y., Adams, G., Wagner, H., Kish, J., Ensley, J., Schuller, D. and Forastiere, A. (2003) A phase III comparison of standard radiation therapy (RT) versus RT plus concurrent cisplatin (DDP) versus split-course RT plus concurrent DDP and 5FU in patients with unresectable squamous cell. Journal of Clinical Oncology, 21, 92-8.
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3. Braustein, A., Li, Y., Hirschland, D. and Edington, D. (2001) Internal associations among Health-risk Factors and Risk Prevalence. American Journal of Health Behavior, 25, 407-417.
2. Berbeau, E., Li, Y., Emmons, K., Youngstrom, R. and Sorensen, G. (2001) Working with Taft-Hartley funds to promote smoking cessation among unionized works. American Journal of Public Health, 91, 1412-15.
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