Survival Analysis by Dr. David G. Kleinbaum and Prof. Matthew Strickland

Do telephone poles need to be treated with creosote every 2 years?  Or will every 5 years do? Do heart patients do better with plain stents, or stents coated with medication?  We typically measure the outcomes of treatments like this in terms of "survival times" (in medicine) or "time to event" (more broadly).  Our online course in the subject is called "Survival Analysis," by David Kleinbaum and Matthew Strickland, and runs from March 23 - April 20. For more details please visit at http://www.statistics.com/survival.

"Survival Analysis" describes the various methods used for modeling and evaluating survival data, also called time-to-event data.  Survival models are used in a variety of health and social sciences, including biostatistics, epidemiology, anthropology, sociology, psychology and economics.  In engineering applications, the topic is called "time-to-failure" 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.

Dr. Kleinbaum is internationally known for his textbooks in statistical and epidemiologic methods and as an outstanding teacher.  His popular text "Survival Analysis - A Self Learning Text" is the text for this course. He has also taught over 150 short courses over the past 30 years throughout the world.

Prof. Mathew Strickland is Assistant Professor in the Department of Environmental and Occupational Health at Emory University, and will be the primary discussion leader for this session of the course.  He has taught a variety of in-person and distance education courses on Epidemiologic Modeling, Fundamentals of Epidemiology, and Maternal/Child Health Epidemiology. Participants can ask questions and exchange comments with the instructors via a private discussion board throughout the period.

Aim of the course –

This course 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 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, SPSS, R, or S+) to analyze survival analysis data.

Who should take this course –

Investigators designing, conducting or analysing medical studies or clinical trials. Researchers in any field (including engineering) working with data on how long things last.

Course Program:

Course outline: The course is structured as follows

SESSION 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 theory
• Descriptive statistics for survival analysis- the hazard ratio
• Graphing survival data- Kaplan Meier
• The Log Rank and related tests.

SESSION 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

SESSION 3:

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

SESSION 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 functions for the extended Cox model.

The core strength of these online courses is the instructors. Statistics.com has over 60 instructors, who are recruited based in their expertise various areas in statistics. Most of these instructors are internationally-known experts in their field. These instructors are the subject experts with industry / consulting experience.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.

If you have any query please feel free to call me or write to me. 

For More details contact at

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com