Introduction to Survival Analysis

Survival analysis is a specialized branch of statistics focused on modeling time-to-event data. Unlike traditional regression methods, it uniquely accounts for censoring — when the event of interest (patient death, mechanical failure, customer churn, loan default) has not yet occurred for some subjects before the observation period ends. This course bridges mathematical foundations with heavy hands-on application. Students work with classic clinical trial records, real-world customer retention logs, and predictive maintenance datasets using both R (survival, cmprsk) and Python (lifelines, scikit-survival). Topics progress from Kaplan-Meier and Nelson-Aalen estimation through Cox regression, AFT models, competing risks, and tree-based machine learning extensions — culminating in three end-to-end applied pipelines across healthcare, business, and engineering.

  • Level: intermediate
  • Duration: 12 weeks
  • 13 sections
  • Price: Free

Topics: survival analysis, time-to-event data, censoring, truncation, Kaplan-Meier, Nelson-Aalen, log-rank test, Cox proportional hazards, hazard ratio, Schoenfeld residuals, accelerated failure time, Weibull distribution, competing risks, cumulative incidence function, Fine-Gray model, random survival forests, concordance index, lifelines, scikit-survival, R survival package, clinical trials, customer churn, predictive maintenance

What's inside

  1. Introduction to Survival Analysis
  2. Chapter 1: Foundations of Time-to-Event Data
  3. Chapter 2: Non-Parametric Estimation: Kaplan-Meier
  4. Chapter 3: Non-Parametric Estimation: Nelson-Aalen and Hazard Smoothing
  5. Chapter 4: Non-Parametric Group Comparisons: Log-Rank Tests
  6. Chapter 5: Semi-Parametric Regression: The Cox Proportional Hazards Model
  7. Chapter 6: Extending the Cox Model: Interactions, Stratification, and Frailty
  8. Chapter 7: Model Diagnostics and Assumption Testing
  9. Chapter 8: Parametric Survival Models
  10. Chapter 9: Accelerated Failure Time Models
  11. Chapter 10: Competing Risks Analysis
  12. Chapter 11: Machine Learning Approaches to Survival Analysis
  13. Chapter 12: Applied Survival Analysis — End-to-End Projects

Read a free sample chapter