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Survival analysis concerns the time from a well-defined origin to some event of interest, such as the time from surgery to death of a cancer patient, the time from wedding to divorce, and the time between the first and second suicide attempts. Although originated in research on the active lifetime of light bulbs and other electric devices, modern applications of survival analysis include many non-survival events. Thus, survival analysis may be more appropriately called the time-to-event analysis.  Like all other statistical methods, survival analysis deals with random events such as death, divorce and suicide. Thus, the time to the event of interest is a nonnegative random variable, and usually right skewed because most events tend to occur in close proximity to each other after a period of time. In survival analysis we are primarily interested in the distribution of survival time and the influence on such a distribution of some explanatory variables such as age and gender. In some applications, data may also be clustered, or correlated, because of certain common features such as genetic traits or shared environmental factors. Clustered survival time data also arise from analyses involving multiple occurrences of an event from the same individual, such as repeated suicide attempts.  Unlike their applications in randomized controlled trials, there are more issues to consider when applying survival analysis to observational data. To illustrate the principles involved, this article uses data from a study of the United States Air Force (USAF) active duty personnel during fiscal years 2004-2009, focusing on suicide in this population and on their utilization of medical services. We discuss how to assess the effect of demographic characteristics (e.g., gender), time-dependent factors (e.g., job characteristics), and changes of mental health conditions (e.g., depression and anxiety) on the risk of suicide. We also discuss how to analyze the impact of shared environmental factors such as organizational culture on the risk of suicide by accounting for data clustering due to nested data structures.

Survival analysis for observational and clustered data: an application for assessing individual and environmental risk factors for suicide.