survival-analysislisted

# Survival Analysis Patterns ## Overview Comprehensive survival analysis methods in R covering Kaplan-Meier estimation, Cox proportional hazards models, parametric survival models, and advanced techniques for time-to-event data. ## Basic Survival Objects ### Creating Survival Data ```r library(survival) # Right-censored data (most common) surv_obj <- Surv(time = df$time, event = df$status) # Left-truncated (delayed entry) surv_obj <- Surv(time = df$entry_time, time2 = df$event_time, event = df$status) # Interval censoring surv_obj <- Surv(time = df$left, time2 = df$right, type = "interval2") # Check structure head(surv_obj) ``` ## Kaplan-Meier Estimation ### Basic KM Analysis ```r # Fit Kaplan-Meier km_fit <- survfit(Surv(time, status) ~ 1, data = df) # Summary statistics summary(km_fit) # Median survival km_fit # Survival at specific times summary(km_fit, times = c(12, 24, 36, 48, 60)) ``` ### KM by Groups ```r # Stratified KM km_fit <- survfit(Surv(time, status) ~ treatment, data = df) # Log-rank test survdiff(Surv(time, status) ~ treatment, data = df) # Stratified log-rank survdiff(Surv(time, status) ~ treatment + strata(site), data = df) # Pairwise comparisons pairwise_survdiff(Surv(time, status) ~ treatment, data = df) ``` ### Publication-Quality KM Plots (survminer) ```r library(survminer) # Basic KM plot ggsurvplot(km_fit, data = df) # Full featured plot ggsurvplot( km_fit, data = df, pval = TRUE, # Add p-value conf.int = T