I would like to test the proportional assumption for our data.
At first, I used,
proc phreg data = pfsdays;
model ept*eptcen(1) = trt / rl;
assess var = (trt) ph;
I got : ERROR: Cannot assess the functional form of ATRSORT since it is not
a continuous explanatory variable.
Then I created a numeric var, trtpn, 0, 1 correspoding to two treatment
groups. No error.
The code I used is:
ods graphics on;
proc phreg data = pfsdays;
model ept*eptcen(1) = trtpn / rl;
assess var = (trtpn) ph;
ods graphics off;
I got two plots for the assess statemetns, Cumulative Martingale function form,
and Standardized Score Process Plot.
I cannot copy the plot here. Sorry.
Before day 300, the plot is above 0, fluctuated lot, then after 300, it is still above 0, but almost flat.
My question is: since my trtpn only has two values, 0, 1, so I use it in the
assess statement is correct, or wrong? If it is correct, how to explain the
results? If it is not correct, how to use the “assess” statement to
test the ph assumption?
Since your variable is a binary or categorical you should include it in the CLASS statement. That's the essential difference between your two models.
To assess the proportional hazard assumption I usually use the
assess ph resample;
line only, as most of my variables are categorical. This will produce the standardized score plots. If you specify resample as well it will give you a p value to help determine if your data violates the ph assumption. You can check the docs for more info on those options.
I don't know what it means to check the functional form of a variable .
I got the Log (-log(Surv)) too before also. For the earlier days, the two lines are very close with some touching together; later they seperate widely; however, at those days, maybe there are only a few patients left in the study. Probably, we cannot say the assumption is violated.
The trt is not significant from both proc lifetest and proc phreg; also I got hazard plot of trt from proc lifetest. They are very close in the early days with a few times of crossing.
So, if we report singles HR from the proc phreg output, does it make sense or not?
If the difference between treatments isn't significant the confidence interval for your hazard ratio should include 1, so make sure to include the 95% confidence interval as well.
Message was edited by: Reeza
> If the difference between treatments isn't
> significant the confidence interval for your hazard
> ratio should include 1, so make sure to include the
> 95% confidence interval as well.
> Message was edited by: Reeza
Thanks a lot!
I feel, basically, I am clear now. I got the point. Since the trt is not significant, HR's confident interval should include 1. Our data does include 1, ( 0.674, 1.465).
Also, our survival curve, code is below.
proc lifetest data = days plots=( s ls lls h(bw = 15 kernel = e ) );
It is not significant. The survival curve, till about day 120, trt 1 curve is above trt 2 curve with two times trt 1 curve down till touching together. After day 120, trt2 curve is above trt1 curve with a few times trt 2 down till touching together.
I would like to say from this survival curve, trt 1 is better at first, and trt 2 is better later. Is this opinion correct?