Survival analysis is a statistical method that is used to analyse the time it takes for an event of interest to occur. The survival analysis is also turned into a “Time to Event” analysis. Data analysts and the biomedical research team uses this analysis to measure the lifetimes of a certain population. For example, the study followed from birth to the onset of some disease or the survival time after the diagnosis of some disease. It majorly describes the length of time from a time origin to an endpoint of interest. The major objective of this test is to compare distributions of survival times in different groups of individuals and analyse how much some factors affect the risk of an event of interest.
Analysing survival data means you have completed your dissertation. At SPSS tutor, we have a professional team that is well equipped to test your survey analysis data and share the reporting accordingly.
Let us understand this by taking an example of a 20-year prospective study of patient survival following myocardial infarction. We use the information on event status and follow-up time to estimate a survival function. In the figure below, the outcome is all-cause mortality and the survival function is depicted below.
One that stays close to 1.0 is called a flat survival curve that suggests very good survival, whereas a survival curve that drops sharply toward 0 suggests poor survival.
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