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
the variables X and Y. What I need to do is to develop a model that
include these variables and predict cancer-specific survival using the
competing-risks regression. Then, I shall calculate the "predictions"
of this model at a certain time point, then I shall use these
prediction as an "endpoint", and predict it using a linear regression
model that include the same variables, i.e X and Y. Finally, I use the
coefficients of this final model to develop a nomogram. Is that
correct?

Many thanks again




Should I calculate the "prediction" of the competing-risks regression
model, and then use this "prediction" as an "endpoint" and predict it
using a linear regression by including the same variables as
predictors?

On Fri, Jun 24, 2011 at 1:27 PM, Frank Harrell [via R]
<ml-node+3622291-345449798-247535@n4.nabble.com> wrote:
> 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 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
>
> F.Abdollah, MD
> San-Raffele hospital
> Milan, Italy
>
> Frank Harrell
> Department of Biostatistics, Vanderbilt University
>
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-- 
Firas Abdollah, MD
Dept. of Urology
San Raffaele Hospital
Vita-Salute University,
Via Olgettina 60, 20132, Milan, Italy
Tel. +39 02 2643 7286
Fax. +39 02 2643 7298
E-mail: firas.abdollah@gmail.com


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