OT Futility Analysis

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.

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