R help - May 2010 - [Off topic?] Time dependent Cox model fitting and validation

DeaR users.

<framework>
These days i'm working on fitting an extended Cox model with
time-dependent covariables and possibly time-varying effects. My
data are in counting process format as described in Therneau&Grambsh's
`Modeling Survival Data', page 68. I'm trying to follow Harrell's
`Regression Modeling Strategies' advices for the choice of my  model.
This study aims to the development of a prognostic model, so it'is
primary predictive.

I have to do stepwise model selection and provide a measure of
predictive accuracy. I'm using rms's cph and validate function with
bw=TRUE option.
</framwork>

<questions>
1. Is validate good at resampling from a counting process format
database? Or should i use a somewhat modified version?

2. Why fastbw(fit,"aic") and step(fit) don't select the
same model? step() appears to stop first. I can't manage to get the
stopping rule in the help files.

3. cph seems to be a bit less "permissive" than coxph in parsing the
model formula. Particularly i have some difficulty in modeling
interactions between covariables and time. Am I totally misguided? Is
there any reference on this topic?

Now a theoretical one:

4. Is it somewhat sensible to use cox.zph() and schoenfeld
residuals to investigate which time dependent variables could need a
time interaction parameter for estimating a time-varying effect?
</questions>

Thanks in advance for any advice.

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