We will use statistical software R. Software R is free and can be downloaded from here

Notes: Introduction to survival analysis

Chapter 1: Survival Analysis - Introduction

• Introduction, features of survival data, censoring.
• Survival function, hazard function, cumulative hazard function.

Chapter 2: Non-parametric Methods

• Kaplan-Meier estimate of survival function.
• Standard error of the estimated survivor function, Greenwood's formula.
• R example I    Figure 1    Figure 2
• The log-rank test.    R example II    R example III

Chapter 3: Cox proportional hazards model

• the model and assumptions
• confidence intervals and hypothesis tests
• comparing alternative models
• model selection
• proportional hazards model and the log-rank test
• R example    R example II

Chapter 4: Model Checking for Cox Models

• Residuals: Martingale residuals, Schoenfeld residuals.
• Assessment of model fit: residual plots
• Testing proportional hazards assumptions
• R examples    Figure 1    Figure 2    Figure 3

Chapter 5. Parametric Survival Models

• The Weibull distribution, the exponential distribution
• Fitting a parametric survival model
• The Weibull proportional hazards model

Chapter 6: Accelerated Failure Time Models

• The accelerated failure time (AFT) model
• Parametric AFT models, Weibull model, lognormal model, log-logistic model.
• Fitting AFT models.
• R examples    Figure 1

Chapter 7. Selected Topics

• Longitudinal studies, joint models for longitudinal and survival data, frailty models
• Missing data problems, informative censoring
• Interval censored survival data

Review   

Longitudinal data and joint model

Missing data, dropout, outliers

Bootstrap and model selection