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Survival Analysis

Survival Analysis

This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.


This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data. 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.

  • Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

After completing this course you will be able to describe survival data and format it appropriately for analysis and understanding. You will learn to graph data, specify and fit proportional hazard models, check assumptions and compute hazard ratios. You will understand stratified and fully-extended PH models and how they are applied to real-world datasets.

  • 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 compute 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

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.

Our Instructors

Course Syllabus

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 Proportional Hazards Models

  • 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

The Stratified Cox Model

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

Week 4

Definitions and Examples

  • 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.

Class Dates


03/01/2024 to 03/29/2024
Instructors: Mr. Anthony Babinec
09/20/2024 to 10/20/2024
Instructors: Mr. Anthony Babinec


03/07/2025 to 04/04/2025
Instructors: Mr. Anthony Babinec
09/19/2025 to 10/17/2025
Instructors: Mr. Anthony Babinec


Course participants should have some experience with computer procedures for regression modeling.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Register For This Course

Analysis of Survey Data from Complex Sample Designs

Additional Information


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.

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.


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.

Course Fee & Information

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Academic affiliation?  In most courses you are eligible for a discount at checkout. Use promo code ACADEMIC where prompted during registration.

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Analysis of Survey Data from Complex Sample Designs