Survival Analysis

Dr. David G. Kleinbaum and Prof. Mathew Strickland

Aim of 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 analyzing medical studies or clinical trials. Researchers in any field (including engineering) working with data on how long things last.

For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:

• Biostatistics (epidemiology) - required
• Biostatistics (controlled trials) - required
• Engineering Statistics - elective

Course Program:

The course is structured as follows

The Instructor:

Dr. David G. Kleinbaum is internationally known for his textbooks in statistical and epidemiologic methods and as an outstanding teacher. He is the author of Epidemiologic Research- Principles and Quantitative Methods (Wiley, 1982), Applied Regression Analysis and Other Multivariable Methods, 3rd Edition (Duxbury, 1997), Logistic Regression- A Self-Learning Text, 2nd edition (Springer, 2002), and Survival Analysis- A Self Learning Text, 2nd edition (Springer, 2005). His recent "electronic" textbook ActivEpi and its accompanying ActivEpi Companion Text (Springer, 2003) serve as the texts 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. He has taught a variety of in-person and distance education courses on Epidemiologic Modeling, Fundamentals of Epidemiology, and Maternal/Child Health Epidemiology. He and David Kleinbaum have been teaching the survival analysis course at statistics.com since 2006.

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

In this session of the course, Prof. Matt Strickland will be handling most of the interaction with students on the discussion board.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Mar. 26 - Apr. 23, 2010
Sep. 24 - Oct. 22, 2010
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Intermediate

Prerequisite:

Course participants should be familiar with the basics of inferential statistics and epidemiologic methods (e.g., statistical study designs, confounding, and effect modification). Some applied knowledge of maximum likelihood techniques and logistic regression is desirable. Also, some experience with computer procedures for regression modeling is desirable.

Course Text:

The required text is Survival Analysis- A Self Learning Text, 2nd edition by David G Kleinbaum and Mitchel Klein, Springer Publishers, 2003, which can be ordered directly from the publisher here. Springer offers a 15% discount on this book after providing the code AECT15 (this code is case sensitive) in the Promotion Code field when prompted during checkout time if you are from North or South America. The same code will work for the rest of the world if you order from the North American site, but may result in longer ship time and higher ship cost (alternatively, you can buy from local site with no discount.)

Software:

Many statistical software packages can perform survival analysis (mainly Cox regression). The readings in this course use SAS illustrations, and the exercises require the use of statistical software. Any package that does survival analysis can be used to do the exercises. Model answers to the exercises will illustrate SAS code. There will also be some illustrations and model answers in Stata. Teaching Assistants can be of some help with SAS, JMP, Stata, and R. (Note: If you want to use R with this course, you should have some prior experience and facility with it. If you wish to use R, but no have current expertise in it, you should consider taking one of our introductory R courses before taking this one.) For more information on the above mentioned statistical software, please click here.

Registration:

Register Online - $469
Register Online (academic) - $369 (you must be affiliated with a college, university or high school)

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. Please use this printed registration form, for these and other special orders.

Note: 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.

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