I'm investigating the difference between Regular Season and the Playoffs in hockey using Survival Analysis in R.
My dataset has the variable
time_diff, which is the interval time between goals in hockey,
event_type where 1 is a goal and 0 is no goal and
session where P is for Playoffs and R is Regular Season.
I use this formula here to get a summary output:
survival::coxph(formula = survival::Surv(time_diff, event_type) ~ session, data = df_cox)
Here is the output where
event_type is GOALS:
After this step, I pivoted towards visualization using this line:
# Fit Formula df_cox_surv_fit <- survfit(formula = Surv(time_diff, event_type) ~ session, data = df_cox) # Draw Survival Curve ggsurvplot(df_cox_surv_fit, data = df_cox, pval = TRUE, xlim = c(0, 60), break.x.by = 20, xlab = "Time (Minutes)", pval.coord = c(48, 0.25), legend = "right", legend.title = "Session", legend.labs = c("Playoff", "Regular Season"))
Is there a difference between fitting model using survfit and coxph? What I'm worried about is having summary output for cox proportional hazards but then visualizing something else using survfit.