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To account for variation (i.e., heteroscedasticity due to cluster) not accounted for by the random effects, we also re-fitted the LMM (i.e., the random intercept model) assuming that the residuals were independent on cluster level.
It may also be difficult to make inferences on cluster level covariates and between-cluster variability.
Activation vs. baseline maps were thresholded at p <.05 corrected on cluster level (cluster-defining threshold t = 4.0).
Monte Carlo simulations were performed on cluster level at P < 0.05 (two-tailed) to correct for multiple comparisons.
(Abbreviations: k cluster size, Z Z-value; activation significant at p < 0.005 uncorrected and p < 0.05 FWE corrected on cluster level).
Figure 1 shows the number of primary care practices per region, as well as the starting moment of the geriatric care model on cluster level.
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This could be so if missingness depends only on cluster-level summaries of and Y.
Analyses were performed on cluster-level summaries using a difference-in-difference (DiD) approach.
Descriptive statistics were also used to compare the IG and CG on cluster-level outcomes.
Statistical analyses were based on cluster-level summaries using a difference-in-difference (DiD) approach and adjusted for imbalances between arms at baseline.
Analyses were based on cluster-level summaries (ie, village means), since the number of clusters is small and because statistical models that allow for clustering (eg, random effects or generalized estimating equations) may be less robust under these circumstances [ 19].
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