Survival Analysis In R - r - learn r - r programming
- Survival analysis deals with predicting the time when a specific event is going to occur. It is also known as failure time analysis or analysis of time to death.
- For example predicting the number of days a person with cancer will survive or predicting the time when a mechanical system is going to fail.
- The R package named survival is used to carry out survival analysis.
- This package contains the function Surv() which takes the input data as a R formula and creates a survival object among the chosen variables for analysis.
- Then we use the function survfit() to create a plot for the analysis.
r programming survival analysis
Install Package
install.packages("survival")
Syntax
- The basic syntax for creating survival analysis in R is −
Surv(time,event)
survfit(formula)
- Following is the description of the parameters used −
- time is the follow up time until the event occurs.
- event indicates the status of occurrence of the expected event.
- formula is the relationship between the predictor variables.
Example
- We will consider the data set named "pbc" present in the survival packages installed above.
- It describes the survival data points about people affected with primary biliary cirrhosis (PBC) of the liver.
- Among the many columns present in the data set we are primarily concerned with the fields "time" and "status".
- Time represents the number of days between registration of the patient and earlier of the event between the patient receiving a liver transplant or death of the patient.
# Load the library.
library("survival")
# Print first few rows.
print(head(pbc))
- When we execute the above code, it produces the following result and chart
id time status trt age sex ascites hepato spiders edema bili chol
1 1 400 2 1 58.76523 f 1 1 1 1.0 14.5 261
2 2 4500 0 1 56.44627 f 0 1 1 0.0 1.1 302
3 3 1012 2 1 70.07255 m 0 0 0 0.5 1.4 176
4 4 1925 2 1 54.74059 f 0 1 1 0.5 1.8 244
5 5 1504 1 2 38.10541 f 0 1 1 0.0 3.4 279
6 6 2503 2 2 66.25873 f 0 1 0 0.0 0.8 248
albumin copper alk.phos ast trig platelet protime stage
1 2.60 156 1718.0 137.95 172 190 12.2 4
2 4.14 54 7394.8 113.52 88 221 10.6 3
3 3.48 210 516.0 96.10 55 151 12.0 4
4 2.54 64 6121.8 60.63 92 183 10.3 4
5 3.53 143 671.0 113.15 72 136 10.9 3
6 3.98 50 944.0 93.00 63 NA 11.0 3
- From the above data we are considering time and status for our analysis.
Applying Surv() and survfit() Function
- Now we proceed to apply the Surv() function to the above data set and create a plot that will show the trend.
# Load the library.
library("survival")
# Create the survival object.
survfit(Surv(pbc$time,pbc$status == 2)~1)
# Give the chart file a name.
png(file = "survival.png")
# Plot the graph.
plot(survfit(Surv(pbc$time,pbc$status == 2)~1))
# Save the file.
dev.off()
- When we execute the above code, it produces the following result and chart −
Call: survfit(formula = Surv(pbc$time, pbc$status == 2) ~ 1)
n events median 0.95LCL 0.95UCL
418 161 3395 3090 3853
- The trend in the above graph helps us predicting the probability of survival at the end of a certain number of days.