[R] bootstrap in time dependent Cox model?

[Terry Therneau]

Two comments.
  I've not found bootstrapping worthwhile for Cox models.  If one has
10-20 events per covariate and no outrageous coefficients (risks of >10
fold) the standard asymptotics are very good.  With an multiple events
AG model there is the additional consideration that no one subject is
responsible for too many of the events.  (Years ago I was working on a
study of recurrent syncope in Long QT syndrome.  Most subjects had 0
events, some 1, and one gentleman 29.  The multi-event analysis could be
summarized as "don't look like him.")

  I like to think of bootstrapping in two stages.  First, the "best"
answer which is what I would get if I had enough money to repeat the
study 100 times.  Then set up the bootstrap to resemble that.  In your
case, what would the pattern of weights be in the hypothetical repeat?
For instance, if your weights are sampling weights from a structured
design, the repeat might have the same set of weights and creating a
bootstrap strategy to replicate it will be more challenging.

Terry T.

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I am wondering how to perform a bootstrap in R for the weighted time
dependent Cox model? (Andersen?Gill format, with multiple observations
from each patients) to obtain the bootstrap standard error of the
treatment effect.