Survival Data Mining

Survival Data Mining

Qiyang Chen (Montclair State University, USA)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch290
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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 only happen at most 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 the method is used for (Elashoff, 1997). Survival Data Mining (SDM) is a new term being coined recently (SAS, 2004). There are many models and variations on the different models for SA or failure analysis. This chapter 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, there are multiple software packages to assist in performing the necessary analyses much more quickly.
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Main Focus

The two biggest pitfalls in SA is the considerable variation in the risk across the time interval which demonstrates the need for shorter time intervals and censoring. Censored observations occur when there is a loss of observation. This most often arises when subjects withdraw or are lost from follow-up before the completion of the study. The effect of censoring often renders a bias within studies based upon incomplete data or partial information on survival or failure times.

There are four basic approaches for the analysis of censored data: complete data analysis; the imputation approach; analysis with dichotomized data; and the likelihood-based approach (Leung et al., 1997). The most effective approach to censoring problems is to use methods of estimation that adjust for whether or not an individual observation is censored. These “likelihood-based approaches” include the Kaplan-Meier estimator and the Cox-regression, both popular methodologies. The Kaplan-Meier estimator allows for the estimation of survival over time even for populations that include subjects who enter at different times or drop out.

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