Survival analysis
Graduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Survival data basics
- Censoring and truncation
- Survival and hazard functions
- Kaplan–Meier estimator
- Nelson–Aalen cumulative hazard
- Comparison of survival curves: log-rank test
Regression models
- Cox proportional hazards model
- Partial likelihood estimation
- Time-dependent covariates (introduction)
- Accelerated failure time models (overview)
- Model checking: Schoenfeld residuals
Advanced topics
- Competing risks (introduction)
- Frailty models (overview)
- Sample size for survival studies
- Power under proportional hazards
STEM / applied
Applied survival analysis
- Analyzing clinical trial data in R (survival package)
- Reporting hazard ratios and survival curves
- Left truncation and immortal time bias
- Reliability analysis in engineering
- Joint models preview (longitudinal + survival)
- Communicating survival results to clinicians
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Essential for biostatistics and reliability programs. Applied sections use medical and engineering datasets with software emphasis.