R help - Aug 2009 - Competing Risks Regression with qualitative predictor with more than 2 categories

Hello,
I have a question regarding competing risk regression using cmprsk package
(function crr()). I am using R2.9.1. How can I do to assess the effect of
qualitative predictor (gg) with more than two categories (a,b,c) categorie c is
the reference category. See above results, gg is considered like a ordered
predictor !
Thank you for your help
Jan
> # simulated data to test
> set.seed(10)
> ftime <- rexp(200)
> fstatus <- sample(0:2,200,replace=TRUE)
> gg <-
factor(sample(1:3,200,replace=TRUE),1:3,c('a','b','c'))
> cov <- matrix(runif(600),nrow=200)
> dimnames(cov)[[2]] <- c('x1','x2','x3')
> cov2=cbind(cov,gg)
> print(z <- crr(ftime,fstatus,cov2))
convergence:  TRUE 
coefficients:
     x1      x2      x3      gg 
 0.2624  0.6515 -0.8745 -0.1144 
standard errors:
[1] 0.3839 0.3964 0.4559 0.1452
two-sided p-values:
   x1    x2    x3    gg 
0.490 0.100 0.055 0.430 
> summary(z)
Competing Risks Regression

Call:
crr(ftime = ftime, fstatus = fstatus, cov1 = cov2)

     coef exp(coef) se(coef)      z p-value
x1  0.262     1.300    0.384  0.683   0.490
x2  0.652     1.918    0.396  1.643   0.100
x3 -0.874     0.417    0.456 -1.918   0.055
gg -0.114     0.892    0.145 -0.788   0.430


      
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Hi,

You can use `model.matrix' to create the apropriate design matrix for factor
variables.

set.seed(10)

ftime <- rexp(200)

fstatus <- sample(0:2,200,replace=TRUE)

gg <- factor(sample(1:3,200,replace=TRUE),1:3,
c('a','b','c'))

cov <- matrix(runif(600),nrow=200)

dimnames(cov)[[2]] <- c('x1','x2','x3')

cov2 = model.matrix( ~ cov + gg)

print(z <- crr(ftime,fstatus,cov2[, -1]))  # you shouldn't have intercept
in the FG model

Hope this helps,
Ravi. 




____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University

Ph. (410) 502-2619
email: rvaradhan at jhmi.edu


----- Original Message -----
From: kende jan <kendejan at yahoo.fr>
Date: Sunday, August 2, 2009 6:01 am
Subject: [R] Competing Risks Regression with qualitative predictor with more
than 2 categories
To: r-help at r-project.org

> Hello,
>  I have a question regarding competing risk regression using cmprsk 
> package (function crr()). I am using R2.9.1. How can I do to assess 
> the effect of qualitative predictor (gg) with more than two categories 
> (a,b,c) categorie c is the reference category. See above results, gg 
[[elided Yahoo spam]]
>  Thank you for your help
>  Jan
>  
>  > # simulated data to test
>  > set.seed(10)
>  > ftime <- rexp(200)
>  > fstatus <- sample(0:2,200,replace=TRUE)
>  > gg <-
factor(sample(1:3,200,replace=TRUE),1:3,c('a','b','c'))
>  > cov <- matrix(runif(600),nrow=200)
>  > dimnames(cov)[[2]] <- c('x1','x2','x3')
>  > cov2=cbind(cov,gg)
>  > print(z <- crr(ftime,fstatus,cov2))
>  
>  convergence:  TRUE 
>  coefficients:
>       x1      x2      x3      gg 
>   0.2624  0.6515 -0.8745 -0.1144 
>  standard errors:
>  [1] 0.3839 0.3964 0.4559 0.1452
>  two-sided p-values:
>     x1    x2    x3    gg 
>  0.490 0.100 0.055 0.430 
>  > summary(z)
>  Competing Risks Regression
>  
>  Call:
>  crr(ftime = ftime, fstatus = fstatus, cov1 = cov2)
>  
>       coef exp(coef) se(coef)      z p-value
>  x1  0.262     1.300    0.384  0.683   0.490
>  x2  0.652     1.918    0.396  1.643   0.100
>  x3 -0.874     0.417    0.456 -1.918   0.055
>  gg -0.114     0.892    0.145 -0.788   0.430
>  
>  
>        
>  	[[alternative HTML version deleted]]
>  
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