Course Details

Survival Analysis


Course ID : QI-BIO-204
Date : For May 2017 - Sept 2017 classes, in NJ, USA/Bangalore, India (depending on no. of students): By Arrangement with Instructor(s)
Duration : 5 days intensive In-Class Training; 2 days Instructor in Classroom to assist students complete assignments
Location : New Jersey, USA and Bangalore, India (depending on no. of students)
Dates: Class Lecture : By Arrangement with Instructor(s)
For Registration/Pricing, call: : Please call our office in New Jersey at 609-454-5635 or email us at info_india@quantument.com for pricing/registration


Introduction of examples of survival data in medical research, concepts, definitions, nature of survival data, functions that describe survival (distribution function, survival function, density function, hazard function), types of censoring and truncation, non-parametric survival function estimation, non-parametric comparison of survival distributions, proportional hazards models and parametric models.

The objective of the course is to provide an analytical foundation and to present techniques for the statistical analysis of survival data. The choice of statistical software is Statistical Analysis Software (SAS).


  • Completion of Advanced Course in Biostatistics.
  • Completion of Foundation Course in Statistical Analysis Software (SAS) Programming.

Learning Outcomes

After the completion of the course, participants will be able to
  • Have a solid understanding of the statistical methodologies that are appropriate to analyze “time to event” data.
  • Use appropriate procedures in the SAS system to conduct methodologies outlined above and interpret the analytical results.

Topics of Study

  • Introductory Examples of Survival Analysis
  • Definition and Functions that Describe Survival
  • Censoring and Truncation
  • Commonly used Survival Functions and Concept of Cure models
  • Assumptions of the Model in the Analysis of Survival data
  • Nonparametric Estimation of Survival Function
  • Comparison of Survival Distributions (2 or more groups) using Nonparametric Methods
  • Proportional Hazards Regression
  • Parametric Regression Models