Survival Analysis taught by Anthony Babinec

Survival Analysis

taught by Anthony Babinec

Aim of Course:

This online course, "Survival Analysis" describes the various methods used for modeling and evaluating survival data, also called time-to-event data. Survival models are used in biostatistical, epidemiological, and a variety of health related fields. They are also used for research in the social sciences as well as the physical and biological sciences, including, economic, sociological, psychological, political, and anthropological data. Survival analysis also has been applied to the field of engineering, where it typically is referred to as reliability analysis.

General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates. The course will also require participants to use a convenient statistical package (e.g., SAS, JMP, STATA, R, or S+) to analyze survival analysis data.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1:

• An overview of survival analysis methods
• Censoring
• Key terms: survival and hazard functions
• Goals of a survival analysis
• Data layout for the computer
• Data layout for understanding
• Descriptive statistics for survival analysis- the hazard ratio
• Graphing survival data- Kaplan Meier
• The Log Rank and related tests.

WEEK 2:

• Introduction to the Cox Proportional Hazards (PH) model- computer example
• Model definition and features
• Maximum likelihood estimation for the Cox PH model
• Computing the hazard ratio in the Cox PH model
• The PH assumption
• Adjusted survival curves
• Checking the proportional hazard assumption
• The likelihood function for the Cox PH model

WEEK 3:

• Introduction to the Stratified Cox procedure
• The no-interaction Stratified Cox model
• The Stratified Cox model that allows for interaction

WEEK 4:

• Definition and examples of time-dependent variables
• Definition and features of the extended Cox model
• Stanford Heart Transplant Study Example
• Addicts Dataset Example
• The likelihood function for the extended Cox model.

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

In addition to assigned readings, this course also has supplemental readings available online