**1. Objective**

In this tutorial, we will discuss Survival Analysis in R. Along with this, we will also cover syntax, usage, and functions or R survival analysis in detail.

**2. Introduction to Survival Analysis in R**

In R, survival analysis particularly deals with predicting the time when a specific event is going to occur. It is also known as analysis of time to death.

**For example**:

To Predict the number of days a person in the last stage will survive.

We use R package to carry out this analysis.

In R survival package, a function named surv() takes the input data as an R formula. It creates a survival object among the chosen variables for analysis. Thus, after this survfit() is being used to create a plot for the analysis.

**What is R Survival Analysis?**

- Model time to event (esp. failure)and is used in medicine, biology, actuary, finance, engineering, sociology, etc.
- It is able to account for censoring.
- We can also compare between 2+ groups.
- It is able to access relationship between covariates and survival time.

**2.1 Install Package**

install.packages(“survival”)

**Syntax**

surv(time,event)

survfit(formula)

**Description of the parameters used** −

- time is the follow-up time until the event occurs.
- the event indicates the status of occurrence of the expected event.
- the formula is the relationship between the predictor variables.

Graphical Analysis is also an important part of R.You can follow this below-mentioned link to learn this:

Introduction to Graphical Analysis

**3. Survival Analysis in R**

Let us see the various steps to perform R programming survival analysis:

- Install survival Package: survival >library (survival)
- Create a survival subject: Surv
- Kaplan – Meier Estimator: survfit
- Mantel-Haenzel Test: survdiff
- Cox Model: coxph

Learn more about R- R Introduction

**3.1 Creating the survival object**

Survival object in R is created by Surv function:

**Usage**

>Surv (time, time2, event, type=c

(‘right’, ‘left’, ‘interval’,

(‘right’, ‘left’, ‘interval’,

‘counting’, ‘interval2’), origin=0)

**3.2 Kaplan-Meier Estimator**

- Also known as product-limit estimator
- It is like the censoring version of an empirical survival function
- It generates a stair-step curve
- Variance is estimated by Greenwood’s formula
- It does not account for effect of another covariate

**3.3 Kaplan-Meier Estimator (Cont.)**

**It is Computed by the function**: survfit

**Usage**

>survfit (formula, …)

**3.4 Mantel-Haenzel Test**

- It is also known as a log-rank test.
- It is generated from a sequence of 2×2 tables.
- Conditional independence.
- It is efficient in comparing groups differed by categorical variables, but not continuous ones.

**3.5 Mantel-Haenzel Test (Cont.)**

**Computed by the function**: survdiff

**Usage**

>survdiff (formula, data, subset, na.action, rho=0)

**3.6 Cox Model**

- It is also known as proportional hazard model.
- Here the assumption is quite strong.

**3.7 Cox Model (Cont.)**

**Computed by the function**: coxph

**Usage**:

>coxph (formula, data=, weights,

subset, na. action, init,

control, method=c

(“efron”,”breslow”,”exact”),

singular. ok=TRUE, robust=FALSE,

model=FALSE, x=FALSE,

y=TRUE, …)

**3.8 Cox Model (Cont.)**

**For Baseline**

>pbc.null<-data.frame(age=rep(0,1),

edema=rep(0,1),bili=rep(1,1),albumin

=rep(1,1),protime=rep(1,1))

=rep(1,1),protime=rep(1,1))

>plot(survfit(cfit,newdata=pbc.null),

lwd=2,ylim=c(.99,1),main=’baseline

survivor‘,xlab =’Days’,ylab=

‘Survival’,conf.int=T)

**3.9 Cox Model (Cont.)**

**For mean covariates**

>plot(survfit(cfit),lwd=2,main=

‘fitted survival function at

mean covariates‘, xlab=’Days’,

mean covariates‘, xlab=’Days’,

ylab=’Survival’)

**3.10 Diagnostic of Cox Model**

- Cox model is amazing, but the assumption is strong.
- Schoenfeld residuals etc,

**4. Conclusion**

We have studied R survival analysis in detail. We have also learned its syntax and usages.The most important thing we have studied is its functions which help you to understand its real-life applications.

Good work