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
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