Survival Analysis and Data Mining

Survival Analysis and Data Mining

Qiyang Chen (Montclair State University, USA), Alan Oppenheim (Montclair State University, USA) and Dajin Wang (Montclair State University, USA)
Copyright: © 2005 |Pages: 6
DOI: 10.4018/978-1-59140-557-3.ch202
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Abstract

Survival analysis (SA) consists of a variety of methods for analyzing the timing of events and/or the times of transition among several states or conditions. The event of interest can happen at most only once to any individual or subject. Alternate terms to identify this process include Failure Analysis (FA), Reliability Analysis (RA), Lifetime Data Analysis (LDA), Time to Event Analysis (TEA), Event History Analysis (EHA), and Time Failure Analysis (TFA), depending on the type of application for which the method is used (Elashoff, 1997). Survival Data Mining (SDM) is a new term that was coined recently (SAS, 2004). There are many models and variations of SA. This article discusses some of the more common methods of SA with real-life applications. The calculations for the various models of SA are very complex. Currently, multiple software packages are available to assist in performing the necessary analyses much more quickly.

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