How to analyze multiple failure time survival data?
This FAQ first appeared as an article in STB-49, ssa13, under the heading Analysis of multiple failure-time data with Stata. Minor revisions have subsequently been made. In this article, when a subject experiences one of the events, it still remains at risk for events of different types.
How can I model repeated events survival analysis?
In the marginal model each event is considered as a separate process. The time for each event starts at the beginning of follow up time for each subject. Furthermore, each subject is considered to be at risk for all events, regardless of how many events each subject actually experienced.
How is the analysis of survival data unique?
Analyzing survival data is unique in that the research interest is typically a combination of whether the event has occurred (binary outcome) and when it has occurred (continuous outcome). Appropriate analysis of survival data requires specific statistical methods that can deal with censored data.
What are the assumptions in a survival analysis?
Survival analysis techniques make use of this information in the estimate of the probability of event. An important assumption is made to make appropriate use of the censored data. Specifically, we assume that censoring is independent or unrelated to the likelihood of developing the event of interest.
When do you use the term survival analysis?
Survival analysis is used to analyze data in which the time until the event is of interest. The response is often referred to as a failure time, survival time, or event time. BIOST 515, Lecture 15 1
When does interval censored data occur in survival analysis?
Interval-censored data occurs when the event is observed, but participants come in and out of observation, so the exact event time is unknown. Most survival analytic methods are designed for right-censored observations, but methods for interval and left-censored data are available. What is the question of interest?