Survival Analysis with R and Excel: A Comprehensive Guide for Actuaries
Survival Analysis is a statistical approach to studying the time until a specific event occurs, such as death, relapse, or failure. For actuaries, it’s essential for modeling lifespans, calculating insurance premiums, and assessing risk factors. This tutorial provides a step-by-step guide to conducting Survival Analysis in R, emphasizing Kaplan-Meier estimates, the Cox Proportional-Hazards Model, and integrating Excel for data management and reporting.
Step 1: Install Necessary Packages
Open R or RStudio. Install the survival package using install.packages("survival"), then load it with library(survival).
Step 2: Load and Explore Data
If your data is in Excel, use the readxl package to import it:library(readxl)data <- read_excel("survival_data.xlsx")
Explore the dataset using:summary(data)head(data)
After cleaning or transforming the data, save it back to Excel with:library(openxlsx)write.xlsx(data, "cleaned_survival_data.xlsx")
Step 3: Kaplan-Meier Survival Estimates
Create a survival object using Surv(time = data$time, event = data$status). Fit a Kaplan-Meier model with:km_fit <- survfit(Surv(time = data$time, event = data$status) ~ 1)
Plot the survival curve:plot(km_fit, xlab = "Time", ylab = "Survival Probability", main = "Kaplan-Meier Curve")
Export Kaplan-Meier results to Excel:write.xlsx(data.frame(time = km_fit$time, survival = km_fit$surv), "kaplan_meier_results.xlsx")
Step 4: Cox Proportional-Hazards Model
Fit a Cox Proportional-Hazards Model with:cox_model <- coxph(Surv(time, status) ~ covariate1 + covariate2, data = data)
View model details:summary(cox_model)
Export model results to Excel:write.xlsx(as.data.frame(summary(cox_model)$coefficients), "cox_model_results.xlsx")
Step 5: Log-Rank Test for Comparing Groups
Use survdiff(Surv(time, status) ~ group, data = data) to perform a log-rank test. Save the results to Excel:write.xlsx(as.data.frame(survdiff(Surv(time, status) ~ group, data = data)), "log_rank_test_results.xlsx")
Step 6: Visualizing Survival Curves
Install and load the survminer package:install.packages("survminer")library(survminer)
Create advanced survival plots with:ggsurvplot(km_fit, data = data, conf.int = TRUE, pval = TRUE, risk.table = TRUE)
Save the plot as an image with:ggsave("survival_plot.png")
Step 7: Assessing Model Assumptions
Check the proportional-hazards assumption with:cox.zph_test <- cox.zph(cox_model)plot(cox.zph_test)
If assumptions are violated, consider stratification:stratified_cox <- coxph(Surv(time, status) ~ strata(group) + covariate, data = data)
Conclusion
Survival Analysis in R is a vital tool for actuaries managing risk and predicting future events. By combining Kaplan-Meier estimates, Cox Proportional-Hazards Models, and Excel integration for data handling and reporting, you can create an efficient and powerful workflow. For questions or insights, leave a comment below. Happy analyzing!
