Hi Greg and Paul, I had initially contemplated a solution similar to Greg's, which is simulation.
However, I might just throw out, that if based upon Terry's comments, time varying covariates do not impact the power/sample size considerations for the Cox model, then Schoenfeld's 1983 article in Biometrics would be of value: Sample-Size Formula for the Proportional-Hazards Regression Model David A. Schoenfeld Biometrics, Vol. 39, No. 2. (Jun., 1983), pp. 499-503. Another reference would be: Sample-Size Calculations for the Cox Proportional Hazards Regression Model with Nonbinary Covariates F.Y. Hsieh and Philip W. Lavori Controlled Clinical Trials 21:552–560 (2000 A skillful Google search will find both available online if you don't have access otherwise. Regards, Marc Schwartz On Jul 17, 2012, at 12:33 PM, Greg Snow wrote: > 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...@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 <538...@gmail.com>* wrote: >> >> >> From: Greg Snow <538...@gmail.com> >> Subject: Re: [R] Power analysis for Cox regression with a time-varying >> covariate >> To: "Paul Miller" <pjmiller...@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...@yahoo.com<http://ca.mc1616.mail.yahoo.com/mc/compose?to=pjmiller...@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 mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.