[R] Weights and coxph
mah
harwood262 at gmail.com
Fri Jun 13 20:43:17 CEST 2008
I am confuse by the results of the weights option for coxph. I
replicated each row three times from the help page for coxph in the
data frame test_freq. I had expected that the coefficients,
significance tests, and tests of non-proportionality would yield the
same results for the replicated and non-replicated data, but the
output below shows differences in all three metrics. Is this the
result of a curved response variable? This is likely more of a
conceptual question than a language question, but all help is
sincerely appreciated.
Mike
> test1
$time
[1] 4 3 1 1 2 2 3
$status
[1] 1 NA 1 0 1 1 0
$x
[1] 0 2 1 1 1 0 0
$sex
[1] 0 0 0 0 1 1 1
$wt
[1] 3 3 3 3 3 3 3
> test_freq
time status x sex
1 4 1 0 0
2 4 1 0 0
3 4 1 0 0
4 3 NA 2 0
5 3 NA 2 0
6 3 NA 2 0
7 1 1 1 0
8 1 1 1 0
9 1 1 1 0
10 1 0 1 0
11 1 0 1 0
12 1 0 1 0
13 2 1 1 1
14 2 1 1 1
15 2 1 1 1
16 2 1 0 1
17 2 1 0 1
18 2 1 0 1
19 3 0 0 1
20 3 0 0 1
21 3 0 0 1
> t1 <- coxph( Surv(time, status) ~ x + strata(sex), data=test1, weights=wt)
> summary(t1)
Call:
coxph(formula = Surv(time, status) ~ x + strata(sex), data = test1,
weights = wt)
n=6 (1 observation deleted due to missingness)
coef exp(coef) se(coef) z p
x 1.17 3.22 0.744 1.57 0.12
exp(coef) exp(-coef) lower .95 upper .95
x 3.22 0.311 0.749 13.8
Rsquare= 0.353 (max possible= 0.999 )
Likelihood ratio test= 2.61 on 1 df, p=0.106
Wald test = 2.47 on 1 df, p=0.116
Score (logrank) test = 2.67 on 1 df, p=0.102
> cox.zph(t1)
rho chisq p
x -0.0716 0.00598 0.938
> t_freq <- coxph( Surv(time, status) ~ x + strata(sex), data=test_freq)
> summary(t_freq)
Call:
coxph(formula = Surv(time, status) ~ x + strata(sex), data =
test_freq)
n=18 (3 observations deleted due to missingness)
coef exp(coef) se(coef) z p
x 1.41 4.09 0.756 1.86 0.063
exp(coef) exp(-coef) lower .95 upper .95
x 4.09 0.245 0.929 18.0
Rsquare= 0.185 (max possible= 0.879 )
Likelihood ratio test= 3.69 on 1 df, p=0.0549
Wald test = 3.47 on 1 df, p=0.0626
Score (logrank) test = 3.84 on 1 df, p=0.0499
> cox.zph(t_freq)
rho chisq p
x -0.0697 0.0526 0.819
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