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Survival Analysis in R
4.8(196)14k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate statistical course covers time-to-event data modeling using survival analysis in R. It delivers actionable methods for analyzing duration data across healthcare, employment, and customer retention scenarios, bridging theoretical survival functions with hands-on R code.
Who It's For
Biostatisticians, data analysts, and researchers working with time-to-event or censored duration datasets.
Key Takeaways
- Time-to-Event Data: Understand how survival analysis differs from traditional regression when handling duration and right-censored data.
- Survivor Curves: Compute, plot, and interpret Kaplan-Meier survivor curves to compare group duration distributions.
- Statistical Modeling: Build, evaluate, and interpret Weibull parametric models and Cox proportional hazards regression in R.
About this course
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.
Price
Free
This course is free to enrol.
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