A simulation paper by Steyerberg a few years ago showed that the split half approach is probably too conservative. You're better off estimating an a priori model and then using the bootstrap for validation. If you've "always" had success before wiht the split half method, I'd say you've been a very lucky fellow till now :-)
Mike Babyak Tony Baglioni <[EMAIL PROTECTED]> wrote: > [-- text/plain, encoding 7bit, charset: iso-8859-1, 28 lines --] > > I have a sample of 2590 patients that I have randomly divided into two > groups - one for exploratory work and one for validation. When I check the > randomization process by comparing the groups on 15 predictor variables > there are no significant differences. However, when I develop a model on > one split half and attempt to validate it on the second split half, the > results are abysmal. I have used this same process on other models and > they've always validated. > > When the samples are not significantly different on any of the predictor > variables, what would cause a model to fail to validate? Whilst I know it > is not appropriate, I've re-randomized the split-halfs several times with > the same results. > > Any help much appreciated. > > Tony > > _______________________ > > The Epsilon Group, LLC > 1410 Sachem Place > Charlottesville, VA 22901 USA > 434.975.0097 x302 > 434.975.0477 (fax) > [EMAIL PROTECTED] > www.epsilongroup.com > > . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
