While working on few assignments related to exploring disorder in cohort studies I came across the concept of Survival analysis. It seems very useful in many real life scenarios.

In survival analysis, subjects are generally followed over a certain time period and the focus is on the time at which the event of interest occurs.

Types of Censoring

Right censoring: Consider a survival analysis study with event of interest as getting divorced. Assume subjects are followed in a study for 20 years. Now a subject who does not get divorced (does not experience the event of interest) for the duration of the study is called right censored. The survival time for this person is considered to be at least as long as the duration of the study.

Left censoring: If a subject's lifetime is less than observed duration, is it said to be left censored.

Reference

[1]Survival analysis

[2]Cornell university stats news letter

**What is Survival analysis?***"Survival*.[1] The event of interest can be development of a disease, failure of a mechanical system or a person getting married.**analysis is a branch of statistics which deals with analysis of time duration to until one or more events happen"**In survival analysis, subjects are generally followed over a certain time period and the focus is on the time at which the event of interest occurs.

**Censoring:**Observations are considered censored when the information about their survival time is not complete.Types of Censoring

Right censoring: Consider a survival analysis study with event of interest as getting divorced. Assume subjects are followed in a study for 20 years. Now a subject who does not get divorced (does not experience the event of interest) for the duration of the study is called right censored. The survival time for this person is considered to be at least as long as the duration of the study.

Left censoring: If a subject's lifetime is less than observed duration, is it said to be left censored.

**Analogy with regression:**The concept is pretty much similar to regression with a dependent variable and multiple independent variable. Also we will get similar output containing coefficient, standard error, p-value etc. Then why cant we simply use linear regression instead? Well, because regression is not capable of effectively dealing with censored data.**Difference with regression:**In survival analysis the dependent variable is made up of two variables,- Time to the event of interest
- The event status

- Survival function: It gives us survival probability (chances of event of interests not happening)
- Hazard function: It gives us chances of event happening per unit time, provided the subject has survived in given time.

Reference

[1]Survival analysis

[2]Cornell university stats news letter

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