Survival Analysis in R Programming – Learn to Predict Accurately!
Do you like to predict the future? I often love to predict the future of others. The statistical tasks of predictions have always been around which allow you to know about the future based on the patterns of the past history. One of such techniques that allow you to measure the duration of time till the occurrence of a future event is Survival Analysis using R. It is one of the most interesting ways of evaluating future occurrences. This is why R survival analysis is one of the most popular techniques which we will implement in this blog.
Before we start our tutorial of R survival analysis, I recommend you to revise Logistic Regression.
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What is 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 the analysis of time to death.
To predict the number of days a person in the last stage will survive. We use the R package to carry out this analysis.
In the 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.
i. Install Package in Survival analysis
Types of R Survival Analysis
1. Kaplan Meier Analysis
In 1958, Edward Kaplan and Paul Meier found an efficient technique for estimating and measuring patient survival rates. This estimator which is plotted over time and is based on a mathematical formula to calculate the response. The response can be failure time, survival time or event time. Some of the examples of Kaplan Meier Analysis are –
- Finding out time until the tumor is recurring
- Estimating time until morbidity after there is an intervention in the treatment.
- In industries, it is used to estimate the time until a machine part fails.
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The survival time response is continuous in nature. It is also greater than or equal to 1. We will plot the survival plot using the Kaplan Meier Analysis. We will make use of the ‘lung’ dataset. You can find out more information about this dataset here.
#Author DataFlair library(“survival”) library(“survminer”) data("lung") head(lung)
surv_func <- survfit(Surv(time,status) ~ sex, data = lung) surv_func
You must explore the linear model concept in R
2. Box Cox Model
The Cox Proportional Hazard model is a popular regression model that is used for the analysis of survival data. It was originally used in the medical area to investigate and assess the relationship between the survival times of patients and their corresponding predictor variables.
The Cox Proportional Hazard Model is an alternative to the above discussed Kaplan-Meier model. It works for both the quantitative predictor as well as for the categorical variable. Therefore, we are able to assess the several risk factors that are involved. In this section, we will implement this model using the coxph() function.
box_cox<- coxph(Surv(time, status) ~ age + sex + ph.ecog + ph.karno + pat.karno + meal.cal + wt.loss , data = lung) summary(box_cox)
Wait! You forget to check non-linear regression in R
#DataFlair curve_cox <- survfit(box_cox) plot(curve_cox)
Here completes our tutorial of R survival analysis. We saw installing packages and types of survival analysis. Also, we discussed how to plot a survival plot using Kaplan Meier Analysis. Hope you understand the concept.
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