Survival analysis addresses time-to-event data, which arise routinely in clinical trials and observational follow-up studies. One distinguishing focus of survival analysis is the ability to draw information from incomplete observations of time-to-event responses in real data settings, addressing complications known as censoring, competing risks, and truncation.
Methodology has been well established for traditional types of survival data, where assumptions (such as independent censoring and independent truncation) are deemed reasonable. Techniques such as the Kaplan-Meier curve, log-rank test, and Cox's proportional hazards regression model have been well accepted and are widely used in many areas across biomedical research. Despite the success of these standard survival analysis techniques, there has been increasing attention to their limitations in practical scenarios where their underlying assumptions are considered unrealistic.
There are also many interesting research problems arising from the rapid development of new, high-dimensional data structures applied to new investigative goals. Examples of such problems include assessment of dynamic survival processes, screening and selection of high-dimensional survival predictors, and delineation of fine-tuned or personalized treatment effects on survival. These challenges provide an exciting outlook for survival analysis methodological research in the future, requiring creative integration with other modern developments of statistical techniques.
Dynamic regression provides another research direction currently under active development by department faculty. Classical models, including the proportional hazards model and accelerated failure time model, presume constant effects of covariates. Such constancy assumption, however, is not realistic in many applications where effects of covariates may actually evolve over time. For instance, the effectiveness of an AIDS drug is typically eroded over time due to drug resistance. To address this issue, quantile regression provides a popular and flexible means, allowing covariate effects to vary across data quantiles. Department faculty members are actively involved in developing quantile regression methods that can appropriately handle special features of survival data.
Faculty: Ying Guo, Yijian (Eugene) Huang, Amita Manatunga, Limin Peng, Yuan Liu