BIOST 2150 - APPLIED SURVIVAL ANALYSIS: METHODS AND PRACTICE

Minimum Credits: 3
Maximum Credits: 3
Long Description This course covers fundamental concepts and methods important for the analysis of datasets where the outcome is the time to an event of interest, such as death, disease occurrence, or disease progression. An important feature of survival data is censoring and truncation. Topics include quantities for summarizing and presenting time-to-event data; non-parametric estimation and hypothesis testing methods such Kaplan-Meier estimator and (weighted) log-rank tests; semi-parametric Cox proportional hazards model and other commonly used parametric models; methods for model development and diagnostics, and sample size considerations. Statistical theory is presented in the context of informing practical data analysis; homework assignments and examinations emphasize appropriate analysis strategy and model interpretation. Students are expected to be familiar with fundamental statistical concepts such as probabilities, distributions, likelihood, hypothesis testing, estimation, and inference. Basic knowledge of either SAS or R programming is also required.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

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