Do patients taking the new drug survive longer than others? How fast do people get a new job after getting unemployed? What can I do to make my friends stay on the dancefloor at my party? All these questions require the analysis of time-to-event data, for which we use special statistical methods. This course introduces basic concepts of time-to-event data analysis, also called survival analysis. Learn how to deal with time-to-event data and how to compute, visualize and interpret survivor curves as well as Weibull and Cox models.
What is Survival Analysis?Free
In the first chapter, we introduce the concept of survival analysis, explain the importance of this topic, and provide a quick introduction to the theory behind survival curves. We discuss why special methods are needed when dealing with time-to-event data and introduce the concept of censoring. We also discuss how we describe the distribution of the elapsed time until an event.
Estimation of survival curves
In this chapter, we will look into different methods of estimating survival curves. We will discuss the Kaplan-Meier estimate and the Weibull model as tools for survival curve estimation and learn how to communicate those results through visualization.
In this chapter, we will learn how to estimate and visualize a Weibull model to learn about the effects of covariates on the time-to-event outcome.
In the last chapter, we learn how to compute and interpret Cox models to understand why they are useful and how they differ from Weibull models.
Statistician at LMU Munich
Heidi is a statistics postdoc at LMU Munich. Her research focus is on statistical methods for personalized medicine with the aim of improving treatment of patients. Heidi has collaborated on several R packages and is an assistant editor for the Journal of Statistical Software, where she is responsible for reproducibility checks. She promotes open and reproducible science and sees R and Git as some of the most powerful tools for computational reproducibility in statistics and machine learning. Heidi loves to teach R related topics.