[R] bootstrapped cox regression (rms package)

[Eric Claus]

Hi Frank,
My apologies for not posting the entire script - I have repasted it below.

library(rms)
library(foreign)
temp=read.spss('coxdata.sav', to.data.frame=T)

formula=Surv(months, recidivate) ~ fac1 + fac2 + fac3 + fac4 + fac5 + fac6 +
fac7 + fac8 
fit=cph(formula, data=temp, x=T, y=T) 
val.out=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) 
anova(out) 

 Factor   Chi-Square d.f. P     
fac1  0.27       1    0.6055 
fac2  0.20       1    0.6514 
fac3  0.01       1    0.9338 
fac4  0.05       1    0.8311 
fac5  1.06       1    0.3036 
fac6  0.33       1    0.5647 
fac7  0.81       1    0.3670 
fac8  0.30       1    0.5832 
 TOTAL   1.48       8    0.9930 

for (i in 1:8) {
print(quantile(out$boot.Coef[,i], c(.025, .975)))
}

    2.5%     97.5% 
-9.236751 20.772061 
     2.5%     97.5% 
-8.841030  3.094755 
     2.5%     97.5% 
-1.834436  2.161983 
      2.5%      97.5% 
-0.1800666  0.0871867 
      2.5%      97.5% 
-3.2129636  0.4783566 
       2.5%       97.5% 
-0.04157389  0.07130994 
      2.5%      97.5% 
-0.6415962  0.1001843 
       2.5%       97.5% 
-0.01529467  0.21055259 

Again, the SPSS output I am trying to match is here:
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 

In looking through the SPSS syntax, my colleague is using SIMPLE resampling,
which is doing sampling with replacement from the original data set.  9999
bootstrap replications are being used, the same as what I have used in the
bootcov command.  The piece of the SPSS output that is not clear is the
generation of p-values from the distribution of parameter estimates; spss
appears to be testing the parameter estimate from the original cox
regression, but the method of testing that parameter is not clear.  

Eric





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