  Given a predictor of outcome derived from a high-dimensional dataset,
pre-validation is a useful technique for comparing it to competing predictors
on the same dataset. For microarray data, it allows one to compare a newly
derived predictor for disease outcome to standard clinical predictors on the
same dataset. We study pre-validation analytically to determine if the
inferences drawn from it are valid. We show that while pre-validation generally
works well, the straightforward "one degree of freedom" analytical test from
pre-validation can be biased and we propose a permutation test to remedy this
problem. In simulation studies, we show that the permutation test has the
nominal level and achieves roughly the same power as the analytical test.
