Survival Analysis

Survival Analysis

taught by Anthony Babinec

 

 
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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.

After completing this course, you will be able to:

  • Describe survival data, and the roles played by censoring, and survival and hazard functions
  • Format data appropriately for analysis, and understanding
  • Graph survival data, and the Kaplan - Meier curve
  • Specify and fit the Cox Proportional Hazards model
  • Check the PH assumption, and comput the hazard ratio
  • Add stratification to specify a Stratified Cox model (with and without interaction)
  • Describe the fully-extended Cox model
  • Apply models to the "Addicts" data, and the Stanford Heart Transplant Study
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.

Survival Analysis

Who Should Take This Course:
Investigators designing, conducting or analyzing medical studies or clinical trials. Researchers in any field (including engineering) working with data on how long things last.
Level:
Intermediate
Prerequisite:

Course participants should also have had some experience with computer procedures for regression modeling.
Organization of the Course:
Options for Credit and Recognition:
Course Text:

The required text is Survival Analysis- A Self Learning Text, 3rd edition by David G Kleinbaum and Mitchel Klein.  It may be purchased here

Software:
You should be familiar with R, SAS, SPSS or Stata.  The instructor can comment on all of them, and can offer limited trouble-shooting with SPSS and R.  The Assistant Teachers can help with Stata and SAS.  Illustrations in the text use SAS, and the text also provides help with the other packages.  
 
 For more information on the above mentioned statistical software, please click here.
Instructor(s):

Dates:

September 13, 2019 to October 11, 2019 March 06, 2020 to April 03, 2020 September 11, 2020 to October 09, 2020

Survival Analysis

Instructor(s):

Dates:
September 13, 2019 to October 11, 2019 March 06, 2020 to April 03, 2020 September 11, 2020 to October 09, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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