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Multivariate multilevel prediction regression models for long term complicated grief and depression were fitted.
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We introduced a new multivariate multilevel model via a latent variable approach.
To account for the hierarchical structure of our data (e.g. multiple hourly observations per day, multiple days per nest), analyses were performed using the multivariate multilevel mixed-modeling procedure in MLwiN 2.15 [35].
Table 2 shows the results of the multivariate multilevel analysis.
Multivariate multilevel modeling (adjusted) (Table 2) showed that, after controlling for the variables that were significant in bivariate multilevel modeling.
2. To estimate the adjusted effect, a multivariate multilevel model will be used.
Second, variables with an adjusted univariate p-value < 0.2 were entered into multivariate multilevel logistic regression.
Trends in incidence rates in LINH were calculated using multivariate multilevel repeated models for count data.
Variables that presented p ≤ 0.20 in bivariate analysis were considered for the multivariate multilevel analysis.
The final multivariate multilevel models were adjusted for predisposing, enabling, need factors, and interaction terms.
Multivariate multilevel ANOVA determined factors associated with overall BACS severity scores (Table 5).
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