-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-
bounces at stat.math.ethz.ch] 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