On Nov 19, 2010, at 12:50 PM, David Winsemius wrote:


On Nov 19, 2010, at 12:32 PM, Shi, Tao wrote:

Hi list,

I was trying to use "predict.coxph" to calculate martingale residuals on a test
data, however, as pointed out before

What about resid(fit) ? It's my reading of Therneau & Gramsch [and of help(coxph.object) ] that they consider those martingale residuals.

The manner in which I _thought_ this would work was to insert some dummy cases into the original data and then to get residuals by weighting the cases appropriately. That doesn't seem to be as successful as I imagined:

> test1 <- list(time=c(4,3,1,1,2,2,3,3), weights=c(rep(1,7), 0),
+               status=c(1,1,1,0,1,1,0,1),
+               x=c(0,2,1,1,1,0,0,1),
+               sex=c(0,0,0,0,1,1,1,1))
> coxph(Surv(time, status) ~ x , test1, weights=weights)$weights
Error in fitter(X, Y, strats, offset, init, control, weights = weights, :
  Invalid weights, must be >0
# OK then make it a small number
> test1 <- list(time=c(4,3,1,1,2,2,3,3), weights=c(rep(1,7), 0.01),
+               status=c(1,1,1,0,1,1,0,1),
+               x=c(0,2,1,1,1,0,0,1),
+               sex=c(0,0,0,0,1,1,1,1))
> print(resid( coxph(Surv(time, status) ~ x , test1,weights=weights) ) ,digits=3)
      1       2       3       4       5       6       7       8
-0.6410 -0.5889  0.8456 -0.1544  0.4862  0.6931 -0.6410  0.0509
Now take out constructed case and weights

> test1 <- list(time=c(4,3,1,1,2,2,3),
+               status=c(1,1,1,0,1,1,0),
+               x=c(0,2,1,1,1,0,0),
+               sex=c(0,0,0,0,1,1,1))
> print(resid( coxph(Surv(time, status) ~ x , test1) ) ,digits=3)
     1      2      3      4      5      6      7
-0.632 -0.589  0.846 -0.154  0.486  0.676 -0.632

Expecting approximately the same residuals for first 7 cases but not really getting it. There must be something about weights in coxph that I don't understand, unless a one-hundreth of a case gets "up indexed" inside the machinery of coxph?

Still think that inserting a single constructed case into a real dataset of sufficient size ought to be able to yield some sort of estimate, and only be a minor perturbation, although I must admit I'm having trouble figuring out ... why are we attempting such a maneuver? The notion of "residuals" around constructed cases makes me statistically suspicious, although I suppose that is just some sort of cumulative excess/deficit death fraction.

http://tolstoy.newcastle.edu.au/R/e4/help/08/06/13508.html

predict(mycox1, newdata, type="expected") is not implemented yet. Dieter suggested to use 'cph' and 'predict.Design', but from my reading so far, I'm not
sure they can do that.

Do you other ways to calculate martingale residuals on a new data?

Thank you very much!

...Tao

--
David Winsemius, MD
West Hartford, CT

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