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Table 3 displays the results of the multi-level mixed effects regression analysis.
Mixed effects regression modeled multilevel outcomes across 4 assessment points (i.e., baseline, mid-test, post-test, 6-month follow-up).
To account for nesting of employees within sites and to model the site-level variables, mixed effects regression was implemented using a random intercepts model.
The SDS values were used as response variables in linear mixed effects regression models.
To assess changes in absolute cell counts post-ART, we fit a linear mixed effects regression model.
Obtained from mixed effects regression.
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We analysed the data using linear and generalized linear mixed-effects regression models [50].
Linear mixed-effects regression models with random intercepts were constructed.
Longitudinal nutritional status, QOL and dependence will be analysed with a linear mixed-effects regression model.
Longitudinal QOL will be analysed with a linear mixed-effects regression model.
Separate linear mixed-effects regression models were used for the HOOS and EQ-5D, and a logistic mixed-effects regression model was used for the proportion of patients reporting problems after surgery.
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