Biostatistics Short Course: Statistical Methods for Truncated Time-to-Event Data: Concepts, Methods, and Applications

This course, led by Jing Qian, PhD, University of Massachusetts Amherst, will provide an overview of statistical methods for analyzing time-to-event data subject to truncation. Truncation arises when an event time is observed only if it falls within a specified observation window, resulting in a biased sample from the population of interest. Discussion will focus on classical methods for analyses and cover more recent developments for settings in which classical assumptions or standard sampling structures may not apply. Topics will include estimation and regression under dependent truncation, sequential truncation arising in observational cohort studies with complex sampling schemes, and methods for estimation and regression under sequential truncation.

Instructor:

Jing Qian, PhD, Professor of Biostatistics, University of Massachusetts Amherst

Audience:

The course is intended primarily for biostatisticians, epidemiologists, quantitative researchers, and other investigators who work with time-to-event data and would like to better understand the methodological and practical issues that arise when such data are subject to truncation. Participants should have a basic understanding of probability and statistical inference, along with familiarity with standard survival analysis concepts, including survival and hazard functions, censoring, the Kaplan–Meier estimator, and Cox proportional hazards regression. Prior knowledge of statistical methods for truncated data is not required.

Participants are encouraged to bring a laptop for R demonstrations and optional short hands-on exercises. However, the course is not primarily a computing workshop, and the main content can be followed without running the code. Example code and datasets will be provided.