11.15 Surivival Curves

• Possible to plot (estimated) survival curves for each diagnosis category, adjusting for the other predictors.

• To make these plots, set the values of the other predictors equal to the mean for quantitative variables, and the modal value for factors.

with(BrainCancer,{
  modaldata <- data.frame(
       diagnosis = levels(diagnosis),
       sex = rep("Female", 4),
       loc = rep("Supratentorial", 4),
       ki = rep(mean(ki), 4),
       gtv = rep(mean(gtv), 4),
       stereo = rep("SRT", 4)
       )
  survplots <- survfit(fit.all, newdata = modaldata)
  plot(survplots, xlab = "Months",
      ylab = "Survival Probability", col = 2:5)
  legend("bottomleft", levels(diagnosis), col = 2:5, lty = 1)
})

The book says that the methods used to estimate \(h_0(t)\) are beyond the scope of the book. Documentation for survfit.coxph mentions a ‘Breslow estimator’ which seems to be similar to Kaplan-Meier… something for future study!