 # Survival Analysis

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.

## Process of Survival Analysis

1. Our process starts by reviewing your research questions and examining the assumptions of proportionality
2. Cox Proportional Hazards, Kaplan-Meier Curves methods are then performed to determine the model adequacy mathematically.
3. In the end, a write-up is made where tables are APA formatted, and the analysis is explained to you so that you can present the power survival analysis to the committee members confidently.

## Survival Function

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.

### Sample Survival Curve - Probability Of Surviving

1. x-axis: Time in years,
2. y-axis: The probability of surviving
3. While reading this graph, we calculate the following result:-

### At time zero, the probability of survival is 1.0 that means 100% of participants are alive.

1. The probability of survival is approximately 0.83 or 83% at 2 years
2. The probability of survival is approximately 0.55 or 55% at 10 years
3. The graph indicates that the median survival is approximately 11 years.

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.

### Outcome after the completion of the module

1. Identify applications with time to event outcomes
2. The actuarial approach to construct a life table
3. Construction of a life table using the Kaplan-Meier approach
4. Perform and interpret the log-rank test
5. Interpreting a hazard ratio
6. Interpretation of coefficients in Cox regression analysis