Hi,
I am trying to convert a colleague from using SPSS to R, but am having
trouble generating a result that is similar enough to a bootstrapped cox
regression analysis that was run in SPSS.  I tried unsuccessfully with
bootcens, but have had some success with the bootcov function in the rms
package, which at least generates confidence intervals similar to what is
observed in SPSS.  However, the p-values associated with each predictor in
the model are not really close in many instances.

Here is the code I am using:

formula=Surv(months, recidivate) ~ fac1 + fac2 + fac3 + fac4 + fac5 + fac6
+ fac7 + fac8
fit=cph(formula, data=temp, x=T, y=T)
validate(fit, method="boot", B=9999, bw=F, type="residual", sls=0.05,
aics=0,force=NULL, estimates=TRUE, pr=FALSE)
out=bootcov(fit, B=9999, pr=F, coef.reps=T, loglik=F)
for (i in 1:8) {
print(quantile(out$boot.Coef[,i], c(.025, .975)))
}
anova(out)

variable low CI high CI p-value
fac1 -8.919692 20.800878 .5917
fac2 -8.683579  3.091100 .6381
fac3 -1.848428  2.193492 .9312
fac4 -0.17575426  0.08333277 .8246
fac5 -3.1488578  0.5166171 .2946
fac6 -0.03621405  0.07241772 .5600
fac7 -0.62847922  0.08566296 .3433
fac8 -0.01553286  0.20909384 .5756

The results from SPSS I am trying to match (or come close to matching) are
the following:
variable low CI high CI p-value
fac1 -8.474 20.020 .456
fac2 -8.206 3.093 .524
fac3 -1.829 2.087 .900
fac4 -.173 .083 .749
fac5 -2.945 .450 .143
fac6 -.035 .070 .306
fac7 -.626 .092 .189
fac8 -.017 .203 .247

Sorry if this is a really basic question.  I have searched for several
hours for an explanation, but cannot find anything that explains why the
p-values would be different despite similar confidence intervals.

Thanks in advance,
Eric

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