Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 53, Issue 3, Pages 603-608Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2008.06.010
Keywords
-
Funding
- National Institute Health [MH060447, MH068638]
Ask authors/readers for more resources
Mixed-effects linear regression models have become more widely used for analysis of repeatedly measured outcomes in clinical trials over the past decade. There are formulae and tables for estimating sample sizes required to detect the main effects of treatment and the treatment by time interactions for those models. A formula is proposed to estimate the sample size required to detect an interaction between two binary variables in a factorial design with repeated measures of a continuous outcome. The formula is based, in part,on the fact that the variance of an interaction is four fold that of the main effect. A simulation study examines the statistical power associated with the resulting sample sizes in a mixed-effects linear regression model with a random intercept. The simulation varies the magnitude (Delta) of the standardized main effects and interactions, the intraclass correlation coefficient (rho), and the number (k) of repeated measures within-subject. The results of the simulation study verify that the sample size required to detect a 2 x 2 interaction in a mixed-effects linear regression model is four fold that to detect a main effect of the same magnitude. (C) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available