In this section Life Table Survival Analysis and Kaplan-Meier Survival Analysis are described. When the exact failure time is known, Kaplan-Meier analysis is preferred and Life Table analysis is mostly obsolete. However, it is discussed here for illustrative and educational purposes.

- Survival analysis calculates the cumulative probability of survival for a group of patients between two events: the start date / time and the end date / time.

- In survival analysis it is essential to define a
**‘hard end point’**(binary outcome measure) for the event of interest.

- The most elementary method is life table survival analysis. However, when the exact survival time is known this method is mostly obsolete.

- For medical purposes, the preferred method is Kaplan-Meier survival analysis.

- Patients are called
if the event of interest has occurred.*uncensored*

- All other patients are
the event of interest has not yet occurred, patient are withdrawn from follow up or have been lost to follow up.*censored;*

- In life table analysis,
*censored*patients are at risk of the event of interest for only part of that year.

- The survival curve is stepped at yearly intervals in life table survival analysis.

- In Kaplan-Meier survival analysis, the curve is also
**stepped**. However, the steps are at the times the event of interest has occurred (and are therefore not regular). If there are many of these events, the curve will have a smooth appearance (but when magnified, will always stay stepped!).

- The
**‘tail end’**of the survival curve can give a**wrong impression**if the number of patients remaining in follow up is low.

- The
**five-year survival**is the cumulative probability of being alive after 5 years.

- The
**median survival**is the time it takes for the cumulative survival to be 50%.

The survival package 1 should be installed and loaded to perform survival analysis in R. The DeducerSurvival package 1 may be used with the Deducer GUI.

**Using the console:**

**Create a survival object (ie: mysurvival)***mysurvival<-Surv(FU time, Event)***Fit the survival curve on the survival object (95% conf levels)***mycurve<-survfit(mysurvival~1,conf.int=0.95)***Plot the survival curve***plot(mycurve)*

**Logrank test:**

*mycompare<-survdiff(mysurvival~group)*

**Cox proportional hazards:**

*mycompare<-coxph(mysurvival~group)*

```
```

```
```