For all the [well-written] formula-based modelling functions
(essentially, those that call model.frame and model.matrix to
interpret
the formula) the option "contrasts" controls how factor
variables are parameterized in the model matrix. contr.treatment
makes the baseline the first factor level, contr.SAS makes
the baseline the last, contr.sum makes the baseline the mean,
etc. E.g.,
df <- data.frame(time=sin(1:20)+2,
cens=rep(c(0,0,1), len=20),
var1=factor(rep(0:1, each=10)),
var2=factor(rep(0:1, 10)))
options(contrasts=c("contr.treatment", "contr.treatment"))
coxph(Surv(time, cens) ~ var1 + var2, data=df)
Call:
coxph(formula = Surv(time, cens) ~ var1 + var2, data = df)
coef exp(coef) se(coef) z p
var11 0.1640 1.18 0.822 0.1995 0.84
var21 0.0806 1.08 0.830 0.0971 0.92
Likelihood ratio test=0.05 on 2 df, p=0.974 n= 20, number of
events= 6
options(contrasts=c("contr.SAS", "contr.SAS"))
coxph(Surv(time, cens) ~ var1 + var2, data=df)
Call:
coxph(formula = Surv(time, cens) ~ var1 + var2, data = df)
coef exp(coef) se(coef) z p
var10 -0.1640 0.849 0.822 -0.1995 0.84
var20 -0.0806 0.923 0.830 -0.0971 0.92
Likelihood ratio test=0.05 on 2 df, p=0.974 n= 20, number of
events= 6
options(contrasts=c("contr.sum", "contr.sum"))
coxph(Surv(time, cens) ~ var1 + var2, data=df)
Call:
coxph(formula = Surv(time, cens) ~ var1 + var2, data = df)
coef exp(coef) se(coef) z p
var11 -0.0820 0.921 0.411 -0.1995 0.84
var21 -0.0403 0.960 0.415 -0.0971 0.92
Likelihood ratio test=0.05 on 2 df, p=0.974 n= 20, number of
events= 6
(lm() has a contrasts argument that can override
getOption("contrasts")
and set different contrasts for each variable but coxph() does not
have
that argument.)
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org
] On Behalf Of David Winsemius
Sent: Thursday, December 01, 2011 9:36 AM
To: a.schlic...@nki.nl
Cc: r-help@r-project.org
Subject: Re: [R] What's the baseline model when using coxph with
factor variables?
On Dec 1, 2011, at 12:00 PM, Andreas Schlicker wrote:
Hi all,
I'm trying to fit a Cox regression model with two factor variables
but have some problems with the interpretation of the results.
Considering the following model, where var1 and var2 can assume
value 0 and 1:
coxph(Surv(time, cens) ~ factor(var1) * factor(var2), data=temp)
What is the baseline model? Is that considering the whole population
or the case when both var1 and var2 = 0?
This has been discussed several times in the past on rhelp. My
suggestion would be to search your favorite rhelp archive using
"baseline hazard Therneau", since Terry Therneau is the author of
survival. (The answer is closer to the first than to the second.)
Kind regards,
andi
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
