The occurrence of significant second-order interactions for group characteristics was examined using real data in a randomized controlled trial (RCT). The interactions exist in all RCTs; they could be easily overlooked when using the simple randomization or stratification methods, but could become more obvious when minimization methods are used. Using real data from an RCT, the minimization method enabled balancing the distributions of the four selected stratified factors. Analyses for three-way second-order interactions including six additional potential confounding variables (for a total of 10 variables) presented 8 significant second-order interactions with the treatment groups. Interaction effects need to be evaluated when treatment effects are examined to maximize the power of the treatment effects in any RCTs. A stepwise regression method with piecewise linear functions would be useful to select the significant variables with interaction effects affecting the treatment outcomes in RCTs. Additional ways to handle interaction effects in RCTs are presented in this paper.
- Minimization random allocation method
- Second-order interactions
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics