Applied Survival Analysis
Course Description
This course will introduce students to the core ideas and methods for the analysis of time-to-event data, also called survival data. We will introduce the data challenges of censoring and truncation, and fundamental concepts including the hazard and survivor functions. We will present nonparametric methods such as the Kaplan-Meier estimator of survival, log-rank hypothesis testing, and sample size calculation for survival analysis. We will also introduce semi-parametric regression via the Cox proportional hazards model and advanced topics, including time-varying covariates and non-proportional effects.
Emphasis will be given to clinical research applications, with labs covering coding examples using the statistical software R. ​​
Instructor
Harrison Reeder, PhD
Instructor in Investigation, Biostatistics, Massachusetts General Hospital
Instructor in Medicine, Biostatistics, Harvard Medical School
Harrison Reeder, PhD is an Instructor in Investigation at Massachusetts General Hospital and Instructor of Medicine at Harvard Medical School. At the Harvard T.H. Chan School of Public Health he has taught the course “Applied Survival Analysis” which covers survival analysis methods and applications, and has also taught courses in epidemiology since 2023. He has taught numerous workshops and short courses nationally and internationally on advanced survival analytic methods. His statistical research focuses on the development of survival analysis tools incorporating time-varying and high-dimensional covariates, and addressing semi-competing risks. His clinical research areas include Long COVID, cardiology, substance use, and cognition and aging. His work has been published in JAMA, American Journal of Obstetrics & Gynecology, Biometrics, Biostatistics, and other high-impact journals.