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? Kind regards, andi
What's the baseline model when using coxph with factor variables?
5 messages · William Dunlap, David Winsemius, Andreas Schlicker
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
______________________________________________ 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.
David Winsemius, MD West Hartford, CT
Terry will correct me if I'm wrong, but I don't think the answer to this question is specific to the coxph function. 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-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of David Winsemius Sent: Thursday, December 01, 2011 9:36 AM To: a.schlicker at nki.nl Cc: r-help at 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
______________________________________________ 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.
David Winsemius, MD West Hartford, CT
______________________________________________ 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.
On Dec 1, 2011, at 1:00 PM, William Dunlap wrote:
Terry will correct me if I'm wrong, but I don't think the answer to this question is specific to the coxph function.
It depends on our interpretation of the questioner's intent. My answer was predicated on the assumption that the phrase "baseline model" meant baseline survival function, ... S_0(t) in survival analysis notation.
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-bounces at r-project.org [mailto:r-help-bounces at r-project.org ] On Behalf Of David Winsemius Sent: Thursday, December 01, 2011 9:36 AM To: a.schlicker at nki.nl Cc: r-help at 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
______________________________________________ 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.
David Winsemius, MD West Hartford, CT
______________________________________________ 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.
David Winsemius, MD West Hartford, CT
William and David, thanks for your help. The contrasts option was indeed what I was looking for but didn't find. andi
On 01.12.2011 20:56, David Winsemius wrote:
On Dec 1, 2011, at 1:00 PM, William Dunlap wrote:
Terry will correct me if I'm wrong, but I don't think the answer to this question is specific to the coxph function.
It depends on our interpretation of the questioner's intent. My answer was predicated on the assumption that the phrase "baseline model" meant baseline survival function, ... S_0(t) in survival analysis notation.
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-bounces at r-project.org [mailto:r-help-bounces at r-project.org ] On Behalf Of David Winsemius Sent: Thursday, December 01, 2011 9:36 AM To: a.schlicker at nki.nl Cc: r-help at 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
______________________________________________ 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.
David Winsemius, MD West Hartford, CT
______________________________________________ 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.
David Winsemius, MD West Hartford, CT