Designing Multisite Randomized Trials to Detect (Conditional) Indirect Effects

• In Event: Advances in Multilevel Modeling for Complex Data and Study Designs

Abstract

Multisite mediation studies represent an analytic cornerstone in the development and assessment of theories across a broad array of disciplines, as they can critically map out development processes and create opportunities to learn how context constrains or facilitates mediating mechanisms. Despite the widespread use of multisite mediation analyses, a notable missing component is methods for predicting statistical power and sample sizes that position studies to detect such effects. The literature has emphasized the importance of complementing main effect power analyses with mediation effect power analyses (even secondary objectives) because they frequently suggest different designs, sample sizes, and resource allocations. In this study, we develop formulas to predict power and guide the design of multisite studies probing mediation under linear structural formulations.

Authors

• Fangxing Bai, Montana State University

• Benjamin Kelcey, University of Cincinnati

• Amota Ataneka, University of Cincinnati

• Yanli Xie, Florida State University

• Kyle T. Cox, University of North Carolina - Charlotte

• Nianbo Dong, University of North Carolina - Chapel Hill