I would take the line that if they hadn't pre-specified any stopping rules, the only reason to stop is safety or new external data. I would be very suspicious of requests from the steering committee to stop for futility - they should be blinded so why are they thinking futility unless results have leaked? I would argue that they are obliged to finish the trial once they start.
This is an example of the need to sort out these things in advance - look up the stuff from the UK DAMOCLES project. The recent book edited by DeMets et al (Data Monitoring in Clinical Trials: A Case Studies Approach) is a good read on these sorts of issues and I think there is a more statistical book from the same group of authors. As far as software is concerned, futility analysis and conditional power are simply standard analyses with made up data and more-or-less justifiable assumptions. Steve. > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:r-help- > [EMAIL PROTECTED] On Behalf Of Spencer Graves > Sent: 22 February 2006 03:45 > To: Kevin E. Thorpe > Cc: R Help Mailing List > Subject: Re: [R] OT Futility Analysis > > What does this particular Steering Committee think a "futility > analysis" is? Do they have any particular reference(s)? What do you > find in your own literature review? > > If it were my problem, I think I'd start with questions like that. > Your comments suggested to me a confounding of technical and political > problems. The politics suggests the language you need to use in your > response. Beyond that, I've never heard before of a "futility > analysis", but I think I could do one by just trying to be clear about > the options the Steering Committee might consider plausible and then > comparing them with appropriate simulations -- summarized as confidence > intervals, as you suggest. > > And I hope that someone else will enlighten us both if there are > better options available. > > Best Wishes, > spencer graves > p.s. For any attorneys who may read these comments, the suggestions are > obviously warranteed up to the amount you paid for it, which is nothing. > If you follow them and they turn out to be inappropriate, you will pay > the price. I encourage you to share the problems with me, so I can > learn from the experience. However, the limits of my liability are as > already stated. > > Kevin E. Thorpe wrote: > > > I beg your pardon if this is too off topic. I am posting here > > since I hope to find an R solution to my problem. Please indulge > > me while I give a little background about what I'm trying to do. > > > > I'm on a DSMB for a clinical trial. The Steering Committee for the > > trial has asked us to perform a futility analysis on their primary > > outcome which is a time-to-event endpoint. The trial was not designed > > with group sequential methods, nor was any futility analysis spelled > > out in the protocol. Another thing which may be relevant is that > > due to circumstances beyond the investigators' control, the trial > > will stop recruitment prematurely unless there is some compelling > > reason for them to find a way to continue the trial. Lastly, the > > trial has accrued not quite half of the planned sample size. > > > > Admittedly, I don't have a vast amount of experience implementing > > stopping rules. In other protocols I have seen where futility > > analyses have been planned but a group sequential design has not > > otherwise been employed, conditional power has been used for the > > futility rule. So naturally, that was my first thought (although > > I may well be wrong) in this case. I have done RSiteSearch() with > > the following terms (three different searches): > > > > futility analysis > > conditional power > > stochastic curtailment > > > > Nothing that looked relevant to my problem jumped out at me. > > > > I have read, somewhat recently, that there are problems with conditional > > power, although I don't remember the details at the moment. This > > has prompted me to consider other approaches to the problem. > > > > One simple thing that has occurred to me, although I don't know > > what the implications are is to simply look at a confidence > > interval around the hazard ratio for the treatment effect. In > > the event that the CI includes 1 and excludes any clinically > > important difference, I would take that as an indication of > > futility. > > > > I would appreciate your comments on this and to learn of any more > > formal methods, particularly of implementations in R. > > > > Thank you for reading. > > > > Kevin > > > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting- > guide.html ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
