Comparison of survival at specific points in time

Question

I have the following survival data

library(survival) data(pbc) #model to be plotted and analyzed, convert time to years fit <- survfit(Surv(time/365.25, status) ~ edema, data = pbc) #visualize overall survival Kaplan-Meier curve plot(fit) 

Here's how it turned out that the resulting Kaplan-Meyer site looks like

Next, I expect survival for 1, 2, 3 years as follows:

 > summary(fit,times=c(1,2,3)) Call: survfit(formula = Surv(time/365.25, status) ~ edema, data = pbc) 232 observations deleted due to missingness edema=0 time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 126 12 0.913 0.0240 0.867 0.961 2 112 12 0.825 0.0325 0.764 0.891 3 80 26 0.627 0.0420 0.550 0.714 edema=0.5 time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 22 7 0.759 0.0795 0.618 0.932 2 17 5 0.586 0.0915 0.432 0.796 3 11 4 0.448 0.0923 0.299 0.671 edema=1 time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 8 11 0.421 0.1133 0.2485 0.713 2 5 3 0.263 0.1010 0.1240 0.558 3 3 2 0.158 0.0837 0.0559 0.446 

As you can see, the final result shows me 95% confidence intervals between different edema levels, but not real p values. Regardless of whether the confidence intervals coincide or not, I still get a pretty good idea that survival at these times is significantly different or not, but I would like to have exact p values. How can i do this?

Answer 1

Your question: "There are survival rates for one year, different for different categories of edema."

For example, if you are interested in 3-year survival; you only need to focus on this part of the curve (first 3 years of observation), as shown in the figure. The follow-up time for patients who are still alive after 3 years is 3 years (ie, the maximum follow-up time in this analysis): pbc$time[pbc$time > 3*365.25] <- 3*365.25 .

Computing a ranking ranking test using coxph in the survival package (the same package that you already use in your analysis) for this data set will provide you with a P value that indicates whether three-year survival is different between the three groups ( which is very important in this example). You can also use the same model to generate P values ​​and risk factors to associate edema with survival for specific reasons.