Hi,

I'm interested in building a Cox PH model for survival modeling, using 2
covariates (x1 and x2).   x1 represents a 'baseline' covariate, whereas x2
represents a 'new' covariate, and my goal is to figure out where x2 adds
significant predictive information over x1.

Ideally, I could get a p-value for doing this.  Originally, I thought of
doing some kind of likelihood ratio test (LRT), where i measure the
(partial) likelihood of the model with just x1, then with x1 and x2, then it
becomes a LRT with 1 degree of freedom.  But when i use the summary()
function for coxph(), i get the following output (shown at the bottom).

I have two questions:

1) What exactly are the p-values in the Pr(>|z|) representing?  I understand
that the coefficients have standard errors, etc., but i'm not sure how the
p-value there is calculated.

2) At the bottom, where it shows the results of an LRT with 2df, i don't
quite understand what model the ratio is being tested against.  If the
current model has two variables (x1 and x2), and those are the extra degrees
of freedom, then the baseline should then have 0 variables, but that's not
really a Cox model?

thanks for any help.

Brian


> summary(coxph(Surv(myTime,Event)~x1+x2))
Call:
coxph(formula = Surv(myTime, Event) ~ x1 + x2)

  n= 211

      coef exp(coef) se(coef)     z Pr(>|z|)
x1 0.03594   1.03660  0.17738 0.203  0.83942
x2 0.53829   1.71308  0.17775 3.028  0.00246 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

   exp(coef) exp(-coef) lower .95 upper .95
x1     1.037     0.9647    0.7322     1.468
x2     1.713     0.5837    1.2091     2.427

Rsquare= 0.111   (max possible= 0.975 )
Likelihood ratio test= 21.95  on 2 df,   p=1.714e-05
Wald test            = 20.29  on 2 df,   p=3.924e-05
Score (logrank) test = 22.46  on 2 df,   p=1.328e-05

	[[alternative HTML version deleted]]

