R help - May 2009 - How does one plot the -log(log(survival))

R 2.8.1
Windows XP

How does one plot the -log(log(survival)) from a coxph? Survfit does not seem to
be up to the task.
Thanks,
John

John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)

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On Sun, 10 May 2009, John Sorkin wrote:
> R 2.8.1
> Windows XP
>
> How does one plot the -log(log(survival)) from a coxph? Survfit does not
seem
> to be up to the task.
It depends on what you mean.

You can convert the plots to a -log(log(survival)) scale with the option
fun="cloglog". In the example from my previous reply
    plot(survfit(fit, newdata=data.frame(age=c(40,50,60))),
       xscale=365.25, xlab = "Years",
ylab="Survival",fun="cloglog")

This is a bit pointless: since these curves are from a Cox model, they are
exactly parallel on a -log(log(survival)) scale.

People sometimes want to fit a model where they stratify on one covariate and
plot the two baseline hazards. You can do this like:
   sfit<-coxph(Surv(futime, fustat) ~ age+strata(rx), data = ovarian)
   plot(survfit(sfit),fun="cloglog")

One motivation given for doing this is examining the proportional hazards
assumption, but this is not a very good way of doing it. The eye is not very
good at judging whether there is a constant vertical separation between curves,
even before you take into account that the uncertainty varies dramatically along
the curve.  I would use cox.zph() and plot.cox.zph(), which give valid formal
tests and reasonable estimates of how the log hazard ratio varies with time. 
Still, if you want to do log-log plots, the survival package is up to the task.

     -thomas

Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle