Smoothing a survival curve

[BlueNose]

I was still thinking about your previous comment, with the logit, and it will not work, as I always have one observation with probability 1, and so the logit cannot be calculated for it.

Setting the probability back to 1 could work, however further in my program, I wish to add another scenario where I add 2% to the smoothed probabilities, and then I may have 2 probabilities of 1, which will "annoy" proc power.

How do you implement a spline effect in glimmix ?

[EastwoodDC]

The basic code will look like this:

proc glimmix data=yourdata method=quad(qpoints=4); 
  effect splTime = spline(Time / knotmethod=equal(2));
  model Survival = splTime;
  output out=smoothed pred=smooth_survival;
run;

You may need to adjust the Number of knots (1 might be enough).

However, this won't contrain predicted survival to 1.0 at Time=0. I thought this could be done with the Parms statement, but I was mistaken. Instead, you could model the compliment of survival with no intercept, and that should do the trick.

data yourdata2;
  set yourdata;
  csurv = 1 - survival;
run ;

proc glimmix data=yourdata2 method=quad(qpoints=4); 
  effect splTime = spline(Time / knotmethod=equal(2));
  model csurv = splTime / noint;
  output out=smoothed pred=smooth_csurv;
run;

Then take the compliment of csurv to get back to the smoothed survival estimates you want.

I haven't ever tried to do quite what you are doing, as we always present the standard Kaplan-Meier curve. You should consider smoothing the upper and lower confidence bounds as well, to convey the uncertainty that goes along with this curve.