Implementation of exact and approximate randomization tests for polynomial growth curves. Academic Article uri icon

abstract

  • Two stand-alone, menu-driven PC programs, written in GAUSS386i, which compare groups of growth curves in a completely randomized design using either (a) exact or (b) approximate randomization tests, are described, illustrated, and made available to interested readers. The programs accommodate missing data in the context of studies planned to have common times of measurement, but where some of the measurement sequences are incomplete. The measurement whose growth is being monitored need not have a Gaussian distribution. We consider the hypothesis that the mean growth curves in G groups are the same; and either compute the exact P value (exact test), or estimate, and provide a confidence interval for, the P value (approximate test).

published proceedings

  • Int J Biomed Comput

citation count

  • 3

complete list of authors

  • Schneiderman, ED||Willis, SM||Kowalski, CJ||Guo, IY

publication date

  • July 1994