Survival Data Analysis

Survival data analysis is also referred to as event time data analysis, which focuses on study of time to a pre-specified event that is often the primary interest in clinical research. The problem of analyzing time to event data arises in many applied fields, such as medicine, biology, public health, epidemiology, engineering, economics, and demography. The most challenging aspect of survival data analysis rests on the fact that the time to event outcome often subjects to some type of censoring. Statistical modeling of various types of censored survival data and proper inference procedures have been the heated topics in statistical communities since 1980, because of their wide range of applications, particularly in life sciences. 

Our Research:

• Wu, Y., Zhang, Y., and Zhou, J. (2021). A spline-based nonparametric analysis for interval-censored bivariate survival data. Statistica Sinica. org/10.5705/ss.202019.0296.
• Bakoyannis, G., Zhang, Y., and Yiannoutsos, C. (2020). Semiparametric regression and risk prediction with competing risks data under missing cause of failure. Lifetime Data Analysis. 26: 659-684.
• Li, J., Zhang, Y., Bakovannis, G. and Gao, S. (2020). On shared gamma-frailty conditional Markov model for semi-competing risks data. Statistics in Medicine. 39(23): 3042-3058.
• Zheng, C. and Chen, Y. (2020). On a shape-invariant hazard regression model with application to an HIV prevention study of mother-to-child transmission. Statistics in Biosciences. 12: 340-352.
• Zheng, C. and Zheng, Y. (2019). Calibrating variations in biomarker measures for improving prediction with time-to-event outcomes. Statistics in Biosciences. 11: 477-503.
• Wu, Y. and Zhang, Y. (2012). Partially Monotone Tensor Spline Estimation of the Joint Distribution Function with Bivariate Current Status Data. Annals of Statistics. 40(3): 1609-1636.
• Zhang, Y., Hua, L., and Huang, J. (2010). A spline-based semiparametric maximum likelihood estimation method for the Cox model with interval-censored data. Scandinavian Journal of Statistics. 37: 338-354.
• Zhang, S., Zhang, Y., Chaloner, K., and Stapleton, JA. (2010). Copula model for bivariate hybrid censored survival data with application to the MACS study. Lifetime Data Analysis. 16: 231-249.
• Zhang, Y. and Jamshidian, M. (2004). On algorithms for NPMLE of the failure function with censored data. The Journal of Computational and Graphical Statistics. 13: 123-140.
• Zhang, Y., Liu, W., and Zhan Y. (2001). A nonparametric two-sample test of the failure functions with interval censoring Case 2. Biometrika. 38: 677-686.