Plot pairwise_survdiff result table together with combined ggsurvplots

Question

I am ploting two survival curves in combination using ggsurvplot_combine: one for the overall survival and another one for survival by a specific variable. I would like to also show the p-values for the survival comparison of each combination in the same plot. I managed to get the p-values from pairwise_survdiff, but printing the table from $p.value on the combined survival plots has been challenging. I managed to get what I want using grid and gridExtra, but it is quite annoying to add the table in the bottom left of the survival plot (I have to add the position manually). Is there any better way to do this using survminer?

This is an example of the figure that I am attempting to generate (it does not have the overall survival though):

Here is a reprex of what I am attempting to produce:

require(survminer)
require(survival)
require(grid)
require(gridExtra)
data(myeloma)
#Create color object
mycolors1<-c('red3','blue3','green4','darkmagenta','goldenrod4','darkorange','deeppink',
'gray60','darkcyan','darkturquoise')
#Create survival plots
f1<-survfit(Surv(time,event)~1,data=myeloma)
f2<-survfit(Surv(time,event)~myeloma$chr1q21_status,data=myeloma)
fit<-list(Overall = f1, Treatment = f2)
print(ggsurvplot_combine(fit,data=myeloma,pal=c('black',mycolors1[1:nlevels(myeloma$chr1q21_status)])
,legend.title=" ",legend.labs=c('Overall',levels(myeloma$chr1q21_status))
,conf.int=F,title= 'Survival by molecular group',xlab='Time'
,font.main = 20,font.x = 15,font.y = 15,ylab='Cumulative Survival probability'
,risk.table=T,tables.col = "strata"
,risk.table.height = 0.25,ggtheme = theme_bw(),size = 0.75))
#Add pairwise comparison table for survival
pushViewport(viewport(x = 0.25, y = 0.36,just = c("left", "top"),height = 0.05, width = 0.1))
grid.draw(grid.table(symnum(pairwise_survdiff(Surv(time, event) ~ chr1q21_status, data = myeloma)$p.value
,cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1)
,symbols = c("", "", "**", "", "+", "ns ")
,abbr.colnames = F, na = 'N/A')
,theme=ttheme_minimal(
core=list(bg_params = list(fill = 'white', col='black')),
colhead=list(fg_params=list(col='white',fontface=2),
bg_params = list(fill = mycolors1[1:(nlevels(myeloma$molecular_group)-1)], col=NA)),
rowhead=list(fg_params=list(col='white',fontface=2),
bg_params = list(fill = c('white',mycolors1[2:nlevels(myeloma$molecular_group)]), col=NA)))
)) 

Answer 1

This is a test code that will help you.

library(survival)
library(survminer)

data(aml)
aml$x <- as.character(aml$x)
aml[10,3] <- 'SuperMaintained'
aml[11,3] <- 'SuperMaintained'
aml[22,3] <- 'SuperMaintained'
aml[23,3] <- 'SuperMaintained'
aml$x <- factor(aml$x, levels = c('Nonmaintained','Maintained','SuperMaintained'))


fit <- survfit(Surv(time, status) ~ x, data = aml)

res=pairwise_survdiff(Surv(time, status) ~ x, data = aml)
table <- res$p.value

p1 <- ggsurvplot(fit, conf.int = FALSE, surv.median.line = c('hv'), data = aml,  pval = TRUE, risk.table = FALSE) 

p1$plot +
  annotate(geom = "table", x = 140, y = 0.9, label = list(as.data.frame(table)))