Sentence examples for mixed models take from inspiring English sources

Mixed models take into account the correlation between measurements in a given subject and more importantly use the whole information (i.e. all the measurements), providing a greater power than when the outcome is dichotomised as, for example, in logistic regression analysis.

Mixed models take into account both fixed (e.g., day of the menstrual cycle) and random (individual participant) effects.

Linear mixed models take into account the non-independence of repeated-measures data and individual heterogeneity by including random effects in the model.

Statistical analyses were performed using prone position session as statistical unit and mixed models taking into account both multiple prone position sessions by patient and multiple measurements during a prone position session.

Longitudinal trends of the studied parameters under cART were estimated by fitting the linear mixed models, taking into account correlations of repeated measurements within the individual patients.

Classical linear mixed models taking the cluster design into account could not be used because of numerical problems.

Results from the longitudinal analysis with generalized linear mixed models taking into account the repeated measurements design and adjusted for age group of the pool.

Adjusted linear mixed models taking MNA, intervention treatment showed non-significant effect −0.21 (−0, 96; 0.26) in Model 1. Similar results were found in more complex models as in Model 5 with the coefficient −0.18 and respective 95% confidence interval (−0.85; 0.34).

Other than the lower cognitive and functional abilities at baseline observed for the non-completers, which the multivariate mixed models took into account, those patients were similar to the completers regarding the other characteristics.

Even though the mixed models took account of the fact that the two groups were different at baseline, we cannot rule out that this physiological mechanism explained parts of the treatment effect.

The association between different levels of continuity of care and the outcomes will be tested through mixed models taking into account random effects at an hospital level and fixed effects of the same covariates described above for the primary analysis.