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Data analyses were conducted using multilevel methods (generalized linear mixed models) to account for the clustered design.
Using multilevel methods, the data can be analyzed with individuals nested in their geographical locations (12, 19).
Practice effects will be accounted for using multilevel methods.
The regression analysis used multilevel linear modeling because respondents were nested within study communities in the survey data.
In this work, we propose an efficient Bayesian data-worth analysis using a multilevel Monte Carlo (MLMC) method.
In view of the nested nature of the data, we used multilevel analytic methods and assessed state-level random effects.
In view of the nested nature of the data, we used multilevel modeling methods and assessed macro level random effects.
Differences between CM practices and between CM and mainstream GP data were tested using multilevel regression analysis.
Data were analyzed using multilevel logistic regression analysis.
To allow for this data structure, and the binary nature of the outcome variable (choice of psychiatry or not), the analysis was performed using multilevel logistic regression methods.
Data were analyzed using multilevel modeling.
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