Time-to-Event Dataset
A Time-to-Event Dataset is a time-series dataset with time to event attributes.
- AKA: Survival Data, Failure Data.
- See: Survival Analysis.
References
2013
- http://en.wikipedia.org/wiki/Survival_analysis
- More generally, survival analysis involves the modeling of time to event data; in this context, death or failure is considered an "event" in the survival analysis literature – traditionally only a single event occurs for each subject, after which the organism or mechanism is dead or broken. Recurring event or repeated event models relax that assumption. The study of recurring events is relevant in systems reliability, and in many areas of social sciences and medical research.
2011
- (Hosmer Jr. et al., 2011) ⇒ David W. Hosmer Jr, Stanley Lemeshow, and Susanne May. (2011). “Applied Survival Analysis: regression modeling of time to event data, 2nd edition." Wiley-Interscience, ISBN 1118211588
1998
- (Altman & Bland, 1998) ⇒ Douglas G Altman, and J Martin Bland. (1998). “Statistics Notes: Time to Event (survival) Data.” In: BMJ: British Medical Journal. doi:10.1136/bmj.317.7156.468
- QUOTE: In many medical studies an outcome of interest is the time to an event. Such events may be adverse, such as death or recurrence of a tumour; positive, such as conception or discharge from hospital; or neutral, such as cessation of breast feeding. It is conventional to talk about survival data and survival analysis, regardless of the nature of the event. Similar data also arise when measuring the time to complete a task, such as walking 50 metres.
The distinguishing feature of survival data is that at the end of the follow up period the event will probably not have occurred for all patients. For these patients the survival time is said to be censored, indicating that the observation period was cut off before the event occurred. We do not know when (or, indeed, whether) the patient will experience the event, only that he or she has not done so by the end of the observation period.
Censoring may also occur in other ways. Patients may be lost to follow up during the study, or they may experience a “competing” event which makes further follow up impossible. For example, patients being followed to a cardiac event may die from some other disease or in an accident.
- QUOTE: In many medical studies an outcome of interest is the time to an event. Such events may be adverse, such as death or recurrence of a tumour; positive, such as conception or discharge from hospital; or neutral, such as cessation of breast feeding. It is conventional to talk about survival data and survival analysis, regardless of the nature of the event. Similar data also arise when measuring the time to complete a task, such as walking 50 metres.