Survival Analysis in R for Public Health
Master survival analysis techniques using R for public health research. Learn Kaplan-Meier plots and Cox regression analysis.
Master survival analysis techniques using R for public health research. Learn Kaplan-Meier plots and Cox regression analysis.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistical Analysis with R for Public Health Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.5
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Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Run and interpret Kaplan-Meier plots for survival analysis
Perform Cox regression modeling with multiple predictors
Assess model assumptions and fit in survival analysis
Handle real-world public health data challenges
Apply descriptive statistics and graphical methods effectively
Skills you'll gain
This course includes:
0.8 Hours PreRecorded video
9 quizzes
Access on Mobile, Desktop, Tablet
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on survival analysis techniques using R programming for public health applications. Students learn to perform time-to-event analysis, from basic Kaplan-Meier plots to advanced Cox regression models. The curriculum covers essential concepts like censoring, hazard functions, and model assumptions, using simulated hospital patient data for heart failure cases. Practical exercises emphasize data handling, statistical interpretation, and solving common challenges in public health data analysis.
The Kaplan-Meier Plot
Module 1 · 4 Hours to complete
The Cox Model
Module 2 · 2 Hours to complete
The Multiple Cox Model
Module 3 · 2 Hours to complete
The Proportionality Assumption
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Expert in Medical Statistics and Healthcare Quality
Prof. Alex Bottle is a Professor in Medical Statistics and co-director of the Dr Foster Unit at Imperial College London. His research is centered on measuring and understanding variations in healthcare quality and safety through the use of large databases. In addition to his research, Prof. Bottle teaches across various undergraduate and postgraduate programs, supervises and examines PhD students in fields such as surgery, cardiovascular risk, and digital health, and provides statistical training to the UK public sector.
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4.5 course rating
311 ratings
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