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Group-wise activation images were calculated by a mixed effects analysis using Bayesian estimation techniques, FMRIB Local Analysis of Mixed Effects [FILM, [ 41].
The glmer function in the lme4 package of R (R Core Team 2016) was used to perform the linear mixed effects analysis in this study.
The impact of the fixed effects error on the sensitivity of the mixed effects analysis can be mitigated by an optimal choice of er-fMRI design and filtering.
Data were collected at baseline and 6-month intervals over the course of the intervention and were analyzed through mixed effects analysis of variance.
FMRI data were analyzed using the General Linear Model and a standard two-stage mixed effects analysis.
The statistical significance of differences for different variables was determined using a mixed effects analysis of variance [39].
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We employed three related analyses: a least squares mixed-model ANOVA, a maximum likelihood mixed-effects analysis and a Bayesian multilevel (or hierarchical) model using Gibbs Sampling (MCMC).
At the third level a group mixed-effects analysis modelled an independent samples t-test comparing patient and control groups.
We also wish to ensure the adequacy of this sample with respect to the linear mixed-effects analysis.
Overall tests of the change across all three measurements are available from generalized linear mixed-effects analysis, assuming that the count (from 0 to 20 behaviors) is distributed as a Poisson variable.
Higher-level between-subject analysis was carried out using a mixed-effects analysis with the FLAME (FMRIB's Local Analysis of Mixed Effects) tool, part of FSL.
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