[R] OT Futility Analysis

From: Kevin E. Thorpe <kevin.thorpe_at_utoronto.ca>
Date: Sat 18 Feb 2006 - 12:44:37 EST

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 E. Thorpe
Biostatistician/Trialist, Knowledge Translation Program
Assistant Professor, Department of Public Health Sciences
Faculty of Medicine, University of Toronto
email: kevin.thorpe@utoronto.ca  Tel: 416.946.8081  Fax: 416.946.3297

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Received on Sat Feb 18 12:51:59 2006

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