SINFA!

Survival analysis is just another name for time to event analysis. The term survival analysis is used predominately in biomedical sciences where the interest is in observing time to death either of patients or of laboratory animals. Time to event analysis has also been used widely in the social sciences where interest is on analyzing time to events such as job changes, marriage, birth of children and so forth. The engineering sciences have also contributed to the development of survival analysis which is called "reliability analysis" or "failure time analysis" in this field, since the main focus is in modeling the time it takes for machines or electronic components to break down. The developments from these diverse fields have for the most part been consolidated into the field of "survival analysis".

There are certain aspects of survival analysis data, such as censoring and non-normality, that generate great difficulty when trying to analyze the data using traditional statistical models such as multiple linear regression. The non-normality aspect of the data violates the normality assumption of most commonly used statistical model such as regression or ANOVA, etc.  A censored observation is defined as an observation with incomplete information.  There are four different types of censoring possible: right truncation, left truncation, right censoring and left censoring.  Most data used in analyses have only right censoring.  Furthermore, right censoring is the most easily understood of all the four types of censoring and if a researcher can understand the concept of right censoring thoroughly it becomes much easier to understand the other three types.  When an observation is right censored it means that the information is incomplete because the subject did not have an event during the time that the subject was part of the study.  The point of survival analysis is to follow subjects over time and observe at which point in time they experience the event of interest. It often happens that the study does not span enough time in order to observe the event for all the subjects in the study. This could be due to a number of reasons. Perhaps subjects drop out of the study for reasons unrelated to the study (i.e. patients moving to another area and leaving no forwarding address). The common feature of all of these examples is that if the subject had been able to stay in the study then it would have been possible to observe the time of the event eventually.