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Comparison of model- and design-based approaches to detect the treatment effect and covariate by treatment interactions in three-level models for multisite cluster-randomized trials

  • Burak Aydin
  • James Algina
  • Walter L. Leite
Article

Abstract

In this study, we evaluated the estimation of three important parameters for data collected in a multisite cluster-randomized trial (MS-CRT): the treatment effect, and the treatment by covariate interactions at Levels 1 and 2. The Level 1 and Level 2 interaction parameters are the coefficients for the products of the treatment indicator, with the covariate centered on its Level 2 expected value and with the Level 2 expected value centered on its Level 3 expected value, respectively. A comparison of a model-based approach to design-based approaches was performed using simulation studies. The results showed that both approaches produced similar treatment effect estimates and interaction estimates at Level 1, as well as similar Type I error rates and statistical power. However, the estimate of the Level 2 interaction coefficient for the product of the treatment indicator and an arithmetic mean of the Level 1 covariate was severely biased in most conditions. Therefore, applied researchers should be cautious when using arithmetic means to form a treatment by covariate interaction at Level 2 in MS-CRT data.

Keywords

Three-level models Covariate by treatment interaction Design-based Model-based Multisite cluster-randomized trials 

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Copyright information

© Psychonomic Society, Inc. 2018

Authors and Affiliations

  1. 1.School of EducationRecep Tayyip Erdogan UniversityRizeTurkey
  2. 2.School of Human Development and Organizational Studies in EducationUniversity of FloridaGainesvilleUSA

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