R 中调整后的生存曲线来自 Cox Model 在特定协变量值

Question

I'd like to plot adjusted survival curves from a Cox model at specific covariate values. The survfit function in the survival package and ggsurvplot in survminer allow one to easily plot adjusted survival curves from a model, but seem to only do so at mean values of covariates. I would like to plot curves at values that I specify, but can't find a way to easily do this in R . SAS can do this easily by using the BASELINE command in PROC PHREG and I'm looking to be able to do something like this in R .

Answer 1

You can do this "by hand". Here is one possibility using the example data from coxph() . Essentially, you need to make a dataset that holds constant all model variables at values you want to use and then vary time from the minimum to maximum in the data (here 0:4). Then, you can predict the survival probabilities for these data and plot.

  library(ggplot2)
  library(survival)
  test1 <- list(time=c(4,3,1,1,2,2,3), 
              status=c(1,1,1,0,1,1,0), 
              x=c(0,2,1,1,1,0,0), 
              sex=c(0,0,0,0,1,1,1)) 
# Fit a stratified model 
m1 <- coxph(Surv(time, status) ~ x + strata(sex), test1) 

tmp1 <- data.frame(
  time = 0:4, 
  status=0, 
  x = 1, 
  sex = 0)

tmp1$fit <- predict(m1, newdata=tmp1, type="survival")

ggplot(tmp1, aes(x=time, y=fit)) + 
  geom_line() + 
  geom_point() + 
  theme_classic()

Below, we pick a different value of x , generate predictions and put the data together with the prediction data above. This could be done in a single step if you wanted.

tmp2 <- data.frame(
  time = 0:4, 
  status=0, 
  x = 2, 
  sex = 0)

tmp2$fit <- predict(m1, newdata=tmp2, type="survival")

tmp <- rbind(tmp1, tmp2)

Now, we could plot both sets of predictions on the same plot.

ggplot(tmp, aes(x=time, y=fit, colour=as.factor(x))) + 
  geom_line() + 
  geom_point() + 
  theme_classic() + 
  labs(colour="X")

^{Created on 2022-04-29 by the reprex package (v2.0.1)}