Survival Analysis and Event Time Modeling with R

Understanding how long it takes for an event to occur is a critical skill in fields ranging from medicine and engineering to finance and social sciences. Whether you call it duration analysis, reliability analysis, or event-time modeling, the ability to predict outcomes over time is essential for modern data professionals. This course provides a structured path to analyzing time-stamped information using R, turning raw data into actionable statistical insights. You will transition from learning basic terminology to applying sophisticated models that handle the unique challenges of survival data, such as censoring and varying time intervals. What you'll learn: - Understand core concepts including censoring, hazard rates, and survival functions - Apply the Kaplan-Meier estimator to estimate and compare survival probabilities - Perform logrank tests to identify significant differences between groups - Build and interpret Cox proportional hazards models with multiple covariates - Implement survival trees as a modern machine learning approach to duration data - Clean and prepare complex date-time information using the lubridate package - Identify and manage missing values and outliers to ensure model reliability The course begins with foundational definitions and the specific data structures required for survival analysis. You will then progress through non-parametric and semi-parametric modeling before exploring advanced data preprocessing workflows and modern machine learning applications for time-to-event data. This course is designed for beginners in statistics or data science who have a basic familiarity with R syntax but are new to duration and reliability analysis. No prior experience with survival modeling is required. Start building robust survival models to solve real-world duration problems today.