New York University 3 credits Global Public Health
This course will provide an introduction to the analysis of survival data, i.e., data subject to incomplete observation due to censoring. Topics include estimation via the Kaplan Meier estimator, comparison of survival data via the log rank and related tests, and regression modeling of survival data using the Cox proportional hazards model and accelerated failure time model. Parametric modeling of survival data will also be covered. Additional topics may include left truncation, competing risks, and study design. Students may select any software package to use for assignments; examples in Stata, SAS and R will be provided. Assignments will involve analysis of survival data from Public Health and biomedical studies. Prerequisites: GPH-GU 3353 or GPH-GU 3332.
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