[R] bootstrapped cox regression (rms package)

[Frank Harrell]

Eric, the output you showed for anova(out) is not correct.  anova.rms does
not produce such output.  Please give us the correct script that obtained
those results and let us know if you are overriding the anova command
somehow.

To your point, make sure that SPSS does not use the bootstrap to obtain a
new point estimate of beta but rather uses the original Cox  beta
coefficients in the test.

Frank

Eric Claus wrote
> 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
> 
> 	[[alternative HTML version deleted]]
> 
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-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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