Dear Firas,
Regression on the subdistribution hazard can be performed by fitting a
weighted Cox model. See Geskus, Biometrics 67, p. 39-49, 2011. Hence, cph
(and coxph) can be used directly; there is no need to use the crr function
in cmprsk. The result from the weighted cph fit should allow you
I will try it out..Thanks a lot
On Mon, Jun 27, 2011 at 10:08 AM, rgeskus [via R]
ml-node+3627184-664831548-247...@n4.nabble.com wrote:
Dear Firas,
Regression on the subdistribution hazard can be performed by fitting a
weighted Cox model. See Geskus, Biometrics 67, p. 39-49, 2011. Hence, cph
Many thanks for the prompt response.
However, I am afraid that it is not completely clear for me. I
apologize, I am not a statistician.
Sorry, may be what I will say make totally non sense, but what I
understood is the following:
Let's suppose that I need to predict cancer-specific survival using
Yes you use the linear predictor from your regression as the dependent
variable in the rms package's ols function. You will get an R^2 of 1.0.
You can depict the ols model with nomogram(). Note that there are so many
statistical issues in competing risks that doing this without a statistician
Many thanks
On Sat, Jun 25, 2011 at 10:00 PM, Frank Harrell [via R]
ml-node+3625011-8897305-247...@n4.nabble.com wrote:
Yes you use the linear predictor from your regression as the dependent
variable in the rms package's ols function. Â You will get an R^2 of 1.0.
 You can depict the ols
Hi R users,
I'd like to draw a nomogram using a competing-risks regression (crr function
in R), rather than a cox regression. However, the nomogram function provided
in the Design package is not good for this purpose.
Do you have any suggestion.
I really appreciate your help
Many thanks
Replace the Design package with the rms package. Use the ordinary linear
regression trick to predict the linear predictor from the competing risk
regression, then use nomogram on this new model (that merely represents the
fit of interest).
Frank
Firas Abdollah wrote:
Hi R users,
I'd like
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