Hello,

I have trouble understanding how do I compare multiple groups in a single survival analysis. When I only have two groups of patients, I simply perform the Kaplan-Meier estimator. How do I deal with supplementary cohorts?

If you look at the colon data, you will find 3 different cohorts (rx column): Obs (observation), Lev (Levamisole treatment) and Lev+5FU (Levamisole + 5-FU treatment).

From: https://www.visualcinnamon.com/2013/07/plotting-survival-analysis-results-in-r.html

The plot demonstrates clear evidence that:

• Obs and Lev cohorts have similar survival levels
• Lev+5FU cohort has higher survival level than Obs and Lev ones

However, since a single P is given (a single value is usually given in multiple online tutorials), I guess that this P represents the higher survival in Lev+5FU compared to others, just like the P given with an ANOVA test states that data is, somehow, different and you need to do additional tests to figure out what is different. Is my thought correct? I actually would like a better measurement of this. What test can I perform to measure survival differences between:

• Obs versus Lev
• Obs versus Lev+FU
• Lev versus Lev+FU

Should I just perform multiple Kaplan-Meier estimators and then adjust P for multiple tests (Bonferroni, BH or whatever) or is there any alternate solution?

I also experienced that if I perform a Cox PH, I do not get full information.

coxph(Surv(time,status) ~ rx, data = colon)

OUT:

Call:
coxph(formula = Surv(time, status) ~ rx, data = colon)

             coef exp(coef) se(coef)     z       p
rxLev     -0.0209    0.9793   0.0768 -0.27    0.79
rxLev+5FU -0.4410    0.6434   0.0839 -5.26 1.5e-07

Likelihood ratio test=35.2  on 2 df, p=2.23e-08
n= 1858, number of events= 920

I guess that the two given P compare Obs versus Lev and Obs versus Lev+5FU right? I my case, no "control" cohort can be used as a survival reference.

Can someone help me with that?