From: Terry Therneau <therneau_at_mayo.edu>

Date: Tue, 11 Nov 2008 08:14:28 -0600 (CST)

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 11 Nov 2008 - 14:20:48 GMT

Date: Tue, 11 Nov 2008 08:14:28 -0600 (CST)

> Similarly, when I do plot(zph), B(t) is fairly non-constant.

> This isn't inherently a problem for me. I don't need a hard single number

*> to characterize the shape of the excess risk. However, I'd like to be
**> able to say
**> something qualitative about the shape of the excess risk for the predictor.
**> E.g., is it linear, monotonically increasing, monotonially decreasing, etc.
**> Is it safe to use the coxph diagnostic plot for this purpose?
*

Basically - yes you can. There are a few caveats:

- As a computational shortcut cox.zph assumes that var(X) is approximately constant over time, where X is the matrix of covariates. (Improving this has been on my to do list for some time). I have found this to be almost always true, but if you have a data set where e.g. everyone in treatment 1 is crossed over at 6 months, then you can get odd results for that covariate. I've run across 2-3 such data sets in 10+ years.
- The spline curve on the plot is "for the eye". You can certainly use other smoothings, fit a line, etc. Often you can find a simpler fit. zpfit <- cox.zph(mycoxfit, transform='identity') plot(zpfit$x, zpfit$y[,1], xlab='Time') #look at variable 1 lines(lowess(zpfit$x, zpfit$y[,1]), col=2) abline( lm(zpfit$y[,1] ~zpfit$x), col=3)

plot(zpfit$x, zpfit$y[,1], log='x') #same as transform=log

etc.

Sometimes the regression spline fit, the default for cox.zph, puts an extra "hook" on the end of the curve, somewhat like polynomials will.

Terry T.

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