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

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).

authors

• Schneiderman, Emet

published proceedings

• Int J Biomed Comput

author list (cited authors)

• Schneiderman, E. D., Willis, S. M., Kowalski, C. J., & Guo, I. Y.

citation count

• 3

complete list of authors

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

publication date

• July 1994

publisher

• Elsevier  Publisher

published in

• International journal of bio-medical computing  Journal

keywords

• Algorithms
• Animals
• Body Height
• Cephalometry
• Child
• Confidence Intervals
• Ethnicity
• Growth
• Humans
• Longitudinal Studies
• Macaca Mulatta
• Male
• Mandible
• Models, Biological
• Models, Statistical
• Random Allocation
• Social Class
• Software

Digital Object Identifier (DOI)

• 10.1016/0020-7101(94)90053-1

start page

• 187

end page

• 192

volume

• 36

issue

• 3

URL

• http://dx.doi.org/10.1016/0020-7101(94)90053-1