On Fri, 15 Oct 2004, Lisa Wang wrote:
Hello,
I wonder when I do coxph in R:
coxph( Surv(start, stop, event) ~ x, data=test)
If x is a categorical varible (1,2,3,4,5), should I creat four dummy varibles for it? if yes, how can I get the overall p value on x other than for each dummy variable?
No. Use coxph( Surv(start, stop, event) ~ factor(x), data=test) or define x as a factor.
For an overal test use anova(): eg
data(pbc) model<-coxph(Surv(time,status)~factor(edtrt)+bili, data=pbc) model
Call: coxph(formula = Surv(time, status) ~ factor(edtrt) + bili, data = pbc)
coef exp(coef) se(coef) z p factor(edtrt)0.5 0.629 1.88 0.2297 2.74 6.2e-03 factor(edtrt)1 1.664 5.28 0.2762 6.02 1.7e-09 bili 0.119 1.13 0.0129 9.29 0.0e+00
Likelihood ratio test=127 on 3 df, p=0 n= 418
anova(model)
Analysis of Deviance Table Cox model: response is Surv(time, status) Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev NULL 418 1746.94 factor(edtrt) 2 62.13 416 1684.82 bili 1 65.11 415 1619.71
summary(model)
Call: coxph(formula = Surv(time, status) ~ factor(edtrt) + bili, data = pbc)
n= 418
coef exp(coef) se(coef) z p factor(edtrt)0.5 0.629 1.88 0.2297 2.74 6.2e-03 factor(edtrt)1 1.664 5.28 0.2762 6.02 1.7e-09 bili 0.119 1.13 0.0129 9.29 0.0e+00
exp(coef) exp(-coef) lower .95 upper .95 factor(edtrt)0.5 1.88 0.533 1.20 2.94 factor(edtrt)1 5.28 0.189 3.07 9.07 bili 1.13 0.887 1.10 1.16
Rsquare= 0.262 (max possible= 0.985 ) Likelihood ratio test= 127 on 3 df, p=0 Wald test = 193 on 3 df, p=0 Score (logrank) test = 281 on 3 df, p=0
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