Survival Analysis in R for Public Health

Understanding how long it takes for an event to occur is critical in public health, whether you are tracking patient recovery times, disease recurrence, or treatment efficacy. Survival analysis provides the specialized statistical tools needed to analyze this time-to-event data accurately. In this written course, you will learn how to perform survival analysis from the ground up using R. You will master key concepts such as censoring and hazard ratios, and learn how to apply modern R workflows to clean, analyze, and interpret real-world health data. What you'll learn: - Understand foundational survival analysis terminology, including censoring, survival functions, and hazard rates. - Calculate survival probabilities using the Kaplan-Meier estimator to track patient outcomes over time. - Compare survival curves across different patient groups using the log-rank test. - Model the impact of multiple risk factors simultaneously using Cox proportional hazards regression. - Apply modern tidyverse principles to clean and prepare time-to-event datasets for analysis. - Interpret statistical outputs to draw meaningful, actionable conclusions for public health reports. The course begins with core theoretical concepts and definitions before guiding you through practical data preparation, curve estimation, and regression modeling in R. This course is designed for public health students, researchers, and data analysts who are new to survival analysis and want to expand their R programming skills. No advanced statistical background is required. Start reading today to unlock deeper insights from your health data.