Lecture

Learning objectives

  1. Define proportional hazards
  2. Perform and interpret Cox proportional hazards regression
  3. Define time-dependent covariates and their use
  4. Identify the differences between parametric and semi-parametric survival models
  5. Identify situations when a parametric survival model might be useful

Outline

  1. Review of survival and hazard functions
  2. The Cox proportional hazards model
    • interpretation and inference
    • what are proportional hazards
    • when hazards aren’t proportional
  3. Parametric vs semi-parametric survival models
  • Vittinghoff sections 6.1-6.2, 6.4

Lab

Learning Objectives

  1. Fit a Cox proportional hazard model
  2. Create a stratified Kaplan-Meier plot
  3. Fit exponential and Weibull accelerated failure time models
  4. Fit a model using strata and a time-dependent covariate
  5. Create a DAG using dagitty

Exercises

  1. Fit a Cox proportional hazard model to the Leukemia 6 MP clinical trial dataset
  2. Create a stratified Kaplan-Meier plot
  3. Fit exponential and Weibull accelerated failure time (AFT) models
  4. Fit a stratified coxph model with a time-dependent covariate using an example from ?coxph
  5. Draw a DAG starting from dagitty.net and re-create it in R