Modelling multilevel nonlinear treatment‐by‐covariate interactions in cluster randomized controlled trials using a generalized additive mixed model

Autor: Sun‐Joo Cho, Kristopher J. Preacher, Haley E. Yaremych, Matthew Naveiras, Douglas Fuchs, Lynn S. Fuchs
Rok vydání: 2022
Předmět:
Zdroj: British Journal of Mathematical and Statistical Psychology. 75:493-521
ISSN: 2044-8317
0007-1102
DOI: 10.1111/bmsp.12265
Popis: A cluster randomized controlled trial (C-RCT) is common in educational intervention studies. Multilevel modelling (MLM) is a dominant analytic method to evaluate treatment effects in a C-RCT. In most MLM applications intended to detect an interaction effect, a single interaction effect (called a conflated effect) is considered instead of level-specific interaction effects in a multilevel design (called unconflated multilevel interaction effects), and the linear interaction effect is modelled. In this paper we present a generalized additive mixed model (GAMM) that allows an unconflated multilevel interaction to be estimated without assuming a prespecified form of the interaction. R code is provided to estimate the model parameters using maximum likelihood estimation and to visualize the nonlinear treatment-by-covariate interaction. The usefulness of the model is illustrated using instructional intervention data from a C-RCT. Results of simulation studies showed that the GAMM outperformed an alternative approach to recover an unconflated logistic multilevel interaction. In addition, the parameter recovery of the GAMM was relatively satisfactory in multilevel designs found in educational intervention studies, except when the number of clusters, cluster sizes, and intraclass correlations were small. When modelling a linear multilevel treatment-by-covariate interaction in the presence of a nonlinear effect, biased estimates (such as overestimated standard errors and overestimated random effect variances) and incorrect predictions of the unconflated multilevel interaction were found.
Databáze: OpenAIRE
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