One quick (though probably not canned) approach to get a feel for what an
analysis might be like is to analyze a sample data set (from the survival
package, a textbook, or a past analysis).  Choose something that has some
similarity to the planned study.  Now look at the widths of the confidence
intervals from that analysis, that will give a feel for the effect size
that can be detected using the same sample size.  You could also analyze a
subset of the data to see what a smaller sample size would give and you
could sample with replacement to get a larger sample and analyze that to
get a feel for larger data sets (this will be more approximate than the
others since you will be reusing subjects and so they won't be as different
from each other as in a true data set).

Terry has also indicated that whether the predictors vary with time or not
should not affect the power/sample size calculations, so if you have a
canned approach (or just simpler approach) for non-varying predictors then
you could just use that.

On Sun, Jul 15, 2012 at 8:02 AM, Paul Miller <pjmiller_57@yahoo.com> wrote:

> Hi Greg,
>
> Thanks for your response. So far I've just been asked to investigate what
> the analysis likely would involve. The hope was that there were be some
> sort of quick and easy "canned" approach. I don't really think this is the
> case though. If I'm asked to do the actual analysis itself, I'll start out
> using the steps you've listed and see where that takes me.
>
> Paul
>
> --- On *Fri, 7/13/12, Greg Snow <538280@gmail.com>* wrote:
>
>
> From: Greg Snow <538280@gmail.com>
> Subject: Re: [R] Power analysis for Cox regression with a time-varying
> covariate
> To: "Paul Miller" <pjmiller_57@yahoo.com>
> Cc: r-help@r-project.org
> Received: Friday, July 13, 2012, 3:29 PM
>
>
> For something like this the best (and possibly only reasonable) option
> is to use simulation. I have posted on the general steps for using
> simulation for power studies in this list and elsewhere before, but
> probably never with coxph.
>
> The general steps still hold, but the complicated part here will be to
> simulate the data.  I would recommend something along the lines of:
>
> 1. generate a value for the censoring time, possibly exponential or
> weibull (for simplicity I would make this not dependent on the
> covariates if reasonable).
> 2. generate a value for the covariate for the given time period
> (sample function possibly), then generate a survival time for this
> covariate value (possibly weibull distribution, or lognormal,
> exponential, etc.)  If the survival time is less than the time period
> and censoring time then you have an event and a time to the event.  If
> the survival time is longer than the censoring time, but not longer
> than the time period (for the covariate), then you have censoring and
> you can record the time to censoring.  If the survival time is longer
> than the time period then you have the row information for that time
> period and can move on to the next time period where you will first
> randomly choose the covariate value again, then generate another
> survival time based on the covariate and given that they have already
> survived a given amount.  Continue with this until you have an event
> or censoring time for each subject.
>
> On Fri, Jul 13, 2012 at 9:17 AM, Paul Miller <pjmiller_57@yahoo.com<http://ca.mc1616.mail.yahoo.com/mc/compose?to=pjmiller_57@yahoo.com>>
> wrote:
> > Hello All,
> >
> > Does anyone know where I can find information about how to do a power
> analysis for Cox regression with a time-varying covariate using R or  some
> other readily available software? I've done some searching online but
> haven't found anything.
> >
> > Thanks,
> >
> > Paul
> >
> > ______________________________________________
> > R-help@r-project.org<http://ca.mc1616.mail.yahoo.com/mc/compose?to=R-help@r-project.org>mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
> > and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Gregory (Greg) L. Snow Ph.D.
> 538280@gmail.com<http://ca.mc1616.mail.yahoo.com/mc/compose?to=538280@gmail.com>
>
>


-- 
Gregory (Greg) L. Snow Ph.D.
538280@gmail.com

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