R help - Apr 2008 - Trend test for survival data

Hello,
is there a R package that provides a log rank trend test
for survival data in >=3 treatment groups?
Or are there any comparable trend tests for survival data in R?

Thanks a lot
Markus

-- 
Dipl. Inf. Markus Kreuz
Universitaet Leipzig
Institut fuer medizinische Informatik, Statistik und Epidemiologie (IMISE)
Haertelstr. 16-18 
D-04107 Leipzig

Tel. +49 341 97 16 276
Fax. +49 341 97 16 109
email: markus.kreuz at imise.uni-leipzig.de

Dear Markus!

Since I did not see an answer yet, my suggestion is to use coxph with 
the groups variable numerically coded as the only independent variable.

Heinz

At 13:39 21.04.2008, Markus Kreuz wrote:
>Hello,
>is there a R package that provides a log rank trend test
>for survival data in >=3 treatment groups?
>Or are there any comparable trend tests for survival data in R?
>
>Thanks a lot
>Markus
>
>--
>Dipl. Inf. Markus Kreuz
>Universitaet Leipzig
>Institut fuer medizinische Informatik, Statistik und Epidemiologie (IMISE)
>Haertelstr. 16-18
>D-04107 Leipzig
>
>Tel. +49 341 97 16 276
>Fax. +49 341 97 16 109
>email: markus.kreuz at imise.uni-leipzig.de
>
>______________________________________________
>R-help at r-project.org mailing list
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.

> Hello,
> is there a R package that provides a log rank trend test
> for survival data in >=3 treatment groups?
> Or are there any comparable trend tests for survival data in R?
  The log-rank test is equivalent to a Cox model with a factor variable as the 
predictor.  To do a trend test, simply fit the Cox model with the variable 
scored as 1,2,3,...  Or fit it as a factor and do a post-hoc trend test:
  
>  fit1 <- coxph(Surv(time, status) ~ ph.ecog, data=lung)
> fit1
Call:
coxph(formula = Surv(time, status) ~ ph.ecog, data = lung)


         coef exp(coef) se(coef)   z       p
ph.ecog 0.476      1.61    0.113 4.2 2.7e-05

Likelihood ratio test=17.6  on 1 df, p=2.77e-05  n=227 (1 observation deleted 
due to missingness)
>  fit2 <- coxph(Surv(time, status) ~ factor(ph.ecog), data=lung)
> fit2
                  coef exp(coef) se(coef)    z       p
factor(ph.ecog)1 0.369      1.45    0.199 1.86 6.3e-02
factor(ph.ecog)2 0.916      2.50    0.225 4.08 4.5e-05
factor(ph.ecog)3 2.208      9.10    1.026 2.15 3.1e-02

Likelihood ratio test=18.4  on 3 df, p=0.000356  n=227 (1 observation deleted 
due to missingness)
> zz <- c(1,2,3)
> test.num <- zz %*% coef(fit2)
> test.var <- zz %*% fit2$var %*% zz
> test.num/sqrt(test.var)
        [,1]
[1,] 2.74323

  Note that ecog performace score=0 is implicitly part of the contrast.  The 
full coefficient vector is (0, .369, .916, 2.208) and my linear contrast zz is 
0,1,2,3.  
  
  	Terry Therneau