Sample Size Requirements for Evaluating Intervention Effects in Three-Level Cluster Randomized Clinical Trials

Moonseong Heo and Mimi Y. Kim
Albert Einstein College of Medicine, Bronx, New York, USA

Series: Mathematics Research Developments
BISAC: MAT000000

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Volume 10

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Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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Experimental clinical trial settings are now often extended to community entities beyond academic research centers. In such settings, a cluster randomized clinical trial (cluster-RCT) design can be useful to rigorously test the effectiveness of a new intervention. At the design stage of the cluster-RCT, it is essential to estimate a sample size sufficient for adequate statistical power to evaluate the different intervention effects. However, the sample size estimation must account for the multilevel data structure that is necessitated by the nature of the cluster-RCT design. This book reviews a three-level data structure and summarizes sample size approaches for testing intervention effects within a unified framework of mixed-effects linear models. (Imprint: Nova)

ABSTRACT

1. INTRODUCTION

2. PRINCIPLES OF STATISTICAL POWER CALCULATION

3. MIXED EFFECTS LINEAR MODEL FOR THREE- LEVEL DATA

4 HYPOTHESIS (I): OVERALL MAIN INTERVENTION EFFECTS

5. HYPOTHESIS (II): TIME-BY-INTERACTION EFFECTS

6. HYPOTHESIS (III): INTERVENTION EFFECTS AT THE END OF TRIAL

7. SUMMARY AND APPLICATIONS

8. DISCUSSION

REFERENCES

INDEX

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