R help - Mar 2009 - Centring variables in Cox Proportional Hazards Model

Dear All,

I am contemplating centering the covariates in my Cox model to reduce
multicollinearity between the predictors and the interaction term and
to render a more meaningful interpretation of the regression
coefficient.  Suppose I have two indicator variables, x1 and x2 which
represent age categories (x1 is patients less than 16 while x2 is for
patients older than 65).  If I use the following Cox model, is there
anyway I can centre the variables?  Do I have to do it before I fit
them into the model and if so, how?

fit2=coxph(Surv(rem.Remtime,rem.Rcens)~x1(partial)+x2(partial),data=partial,method="breslow")

Thank you,

Laura

Laura Bonnett wrote:
> Dear All,
> 
> I am contemplating centering the covariates in my Cox model to reduce
> multicollinearity between the predictors and the interaction term and
> to render a more meaningful interpretation of the regression
> coefficient.  Suppose I have two indicator variables, x1 and x2 which
> represent age categories (x1 is patients less than 16 while x2 is for
> patients older than 65).  If I use the following Cox model, is there
> anyway I can centre the variables?  Do I have to do it before I fit
> them into the model and if so, how?
> 
>
fit2=coxph(Surv(rem.Remtime,rem.Rcens)~x1(partial)+x2(partial),data=partial,method="breslow")
> 
> Thank you,
> 
> Laura
> 
There is no need to center the variables.   Also, you are going to find 
a huge lack of fit for the shape of the age effect you are using, as 
opposed to using a smooth function in continuous age.

Your notation is treating x1 and x2 as functions of data frame which is 
strange.  With data=partial you would ordinarily just have something 
like x1+x2 in the model.

Frank

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University