install.packages(“survival”) install.packages(“survminer”) library(survival) library(survminer) SA_csv <- read_csv(“C:/Users/elgine/Music/SA.csv.xls”) data(lung) str(lung) data(lung) head(lung) #Kaplan-Meier survival km_fit <- survfit(Surv(time, status) ~ sex, data = lung) ggsurvplot(km_fit, data = lung, title = “Kaplan-Meier Survival Curves”, risk.table = TRUE, pval = TRUE) #interpretation Females exhibit higher survival probabilities than males, with a significant difference (p < 0.05) #Kaplan-Meier curve with confidence intervals and a risk table ggsurvplot(km_fit, data = lung, risk.table = TRUE, conf.int = TRUE, xlab = “Time in Days”, ylab = “Survival Probability”, title = “Kaplan-Meier Survival Curve for Lung Cancer Patients”)
#Nelson-Aalen cumulative hazard estimator na_fit <- survfit(Surv(time, status) ~ 1, data = lung, type = “aalen”) summary(na_fit) #interpretation Males accumulate hazard faster, indicating higher risk over time #cumulative hazard using the Nelson-Aalen estimator ggsurvplot(na_fit, fun = “cumhaz”, data = lung, risk.table = TRUE, conf.int = TRUE, xlab = “Time in Days”, ylab = “Cumulative Hazard”, title = “Nelson-Aalen Cumulative Hazard Estimate”)
#log-rank test comparing survival between sexes logrank_test <- survdiff(Surv(time, status) ~ sex, data = lung) logrank_test #interpretation p = 0.00135 < 0.05, confirming survival differs significantly by sex
#Cox proportional hazards model cox_fit <- coxph(Surv(time, status) ~ age + sex + ph.ecog, data = lung) summary(cox_fit) cox_zph <- cox.zph(cox_fit) print(cox_zph) ggcoxzph(cox_zph) #interpretation Sex (HR = 0.588, p < 0.01) and ph.ecog (HR = 2.1, p < 0.001) significantly affect survival; PH assumption holds (p > 0.05) #results The analysis reveals sex and performance score as key survival predictors, with consistent findings across methods.
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