Re: [R] Problem plotting curve on survival curve

From: Terry Therneau <therneau_at_mayo.edu>
Date: Mon, 03 Mar 2008 08:53:14 -0600 (CST)


Calum had a long question about drawing survival curves after fitting a Weibull model, using pweibull, which I have not reproduced.

It is easier to get survival curves using the predict function. Here is a simple example:

> library(survival)
> tfit <- survreg(Surv(time, status) ~ factor(ph.ecog), data=lung)
> table(lung$ph.ecog)

   0 1 2 3 <NA>
  63 113 50 1 1

> tdata <- data.frame(ph.ecog=factor(0:3))
> qpred <- predict(tfit, newdata= tdata, type='quantile', p=1:99/100)
> matplot(t(qpred), 99:1/100, type='l')

  The result of predict is a matrix with one row per group and one column per quantile. The final plot uses "99:1" so as to show 1-F(t) = S(t) rather than F. Don't ask for the 1.0 quantile BTW -- it is infinity and I doubt you want the plot to stretch out that far. The 0.0 quantile can also have issues due to the implicit log transform used in many distributions.

   If I had not used the newdata argument, we would get 227 rows in the result, one for each subject. That is, 63 copies of the ph.ecog==0 curve, 113 of the ph.ecog==1 curve, ... The above fit assumed a common shape for the 4 groups, you can add a "+ strata(ph.ecog)" term to have a separate scale for each group; this would give the same curves as 4 separate fits to the subgroups.   

  There are several advantages to using the predict function. The first is that the code does not need to change if you decide to use a different distribution. The second is that you can add the "se.fit=T" argument to get confidence bounds for the curves. (A couple more lines for your matplot call of course).

	Terry Therneau
	Mayo Clinic

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