Analysis of Interval-Censored Time-to-Event Data: Methods and Applications

Interval-censored time-to-event data are commonly generated in biomedical research in estimating the new treatment effectiveness and studying patient survival in cancer and infectious diseases. However, this type of data is sometimes erroneously analyzed with the traditional Cox regression without considering the interval-censored structure which could produce biased and inefficient estimates. This short course is then designed to present the up-to-date modeling to analyze such data. Using our newly published book (Emerging Topics in Modeling Interval-Censored Survival Data. Springer 2022), we start with an overview of data structures from right-censored, left-censored to interval-censored data and then discuss the associated statistical survival models to analyze these data, including the Cox proportional hazards model, linear transformation models, and their extensions and development. Focusing on the modeling of interval-censored data, statistical procedures will be discussed with the most recent development in methods and software implementations in R/SAS. The specific topics to be discussed include 1) Biases inherent in the common practice of imputing interval-censored time-to-event data, 2) Nonparametric estimation of a survival function, 3) Nonparametric treatment comparisons, 4) Semiparametric regression analysis, 5) Analysis of multivariate interval-censored failure time data, 6) Variable selection and subgroup analysis of interval-censored data, 7) Analysis of high-dimensional interval-censored data.