Optimal Design in Cluster-Randomized Trials of Cross-Level Mediation

• In Event: 25.018 - Advanced Analysis and Inference Issues

Abstract

Recent studies show cluster randomized trials may be well powered to detect mediation or indirect effects in multilevel settings. However, literature has rarely provided guidance on designing cluster-randomized trials aim to assess indirect effects. In this study, we developed closed-form expression to estimate the variance of and the statistical power to detect both individual and contextual indirect effects in cluster-randomized trials (i.e., 2-1-1 mediation). We then investigated the optimal sample allocation under a fixed budget such that researcher can minimize the resources needed for the study while maintaining adequate power. To facilitate end-user calculations, we have also developed freely available software that implements these formulas and the resampling-based approaches.

Authors

• Zuchao Shen, University of Georgia

• Benjamin Kelcey, University of Cincinnati

• Kyle T. Cox, University of Cincinnati

• Jiaqi Zhang, SKT Education Group