SP07
Fast Two-Sample Permutation Tests, Even when One Sample is Large, that Efficiently Maximize Power Under Crude Monte Carlo Sampling
J.D. Opdyke, Economic Consulting Group Group - Andersen, LLP

I present a method for quickly performing multiple nonparametric two-sample permutation tests on continuous data in SAS®, even when one sample is large. I maximize statistical power (within the context of a crude Monte Carlo approach) by "oversampling" - drawing more permutation samples than desired, deleting duplicates, and then selecting the desired number of samples from the remainder. I determine the optimal number of samples to "oversample" based on sampling probability and the runtime of a sampling procedure (PROC PLAN). Implementing "oversampling" with nearly optimal numbers of samples increases start-to-finish runtime typically by only 5%, and always by less than 10%. Using telecommunications performance measurement data from multiple sources wit h a wide range of sample size pairs, I benchmark start-to-finish runtime against a) another SAS procedure (PROC MULTTEST), b) another SAS program written for the same purpose, and c) Cytel’s PROC TWOSAMPL®, with very favorable results. The relative bench mark speeds would be identical if applied to data from randomized controlled studies.