In statistics, the Kolmogorov–Smirnov test (K–S test) is a nonparametric test for the equality of continuous, one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two samples (two-sample K–S test). The Kolmogorov–Smirnov statistic quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions of two samples. The null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from the same distribution (in the two-sample case) or that the sample is drawn from the reference distribution (in the one-sample case). In each case, the distributions considered under the null hypothesis are continuous distributions but are otherwise unrestricted.
Is there a version of KS which could be applied to panel data i.e. where it is known that observations are not independent - same individuals measured over time.
PS. I'm looking at comparing the distributions of two groups of subjects (survivals and deaths) although I have multiple measurements for individual subjects over time...
Potentialy useful page http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm