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Multivariate mixed effects models were used to estimate associations between gene expression and specific gravity adjusted phthalate metabolite concentrations (Fitzmaurice et al. 2004; Verbeke and Molenberghs 2000).
We fit multivariate mixed effects models that simultaneously adjusted for delivery method, time elapsed from delivery and region (inner vs. outer) (Table 2).
A multivariate mixed effects logistic regression was fitted to assess the factors associated with CNIP-induced haemodynamic instability.
Variables having a p-value of <0.2 were selected for further inclusion in multivariate mixed effects logistic regression models.
Initially, risk factors of a short birth interval were determined using a multivariate mixed effects logistic regression model.
Linear mixed effects modeling of eGFR with the lipoprotein of interest was performed in incremental models, and the coefficient determined from multivariate mixed effects analysis represented eGFR slope.
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We used univariate and multivariate mixed effect logistic regressions (19 ) to describe the correlations between treatment phase and surveillance observations.
Subsequently, a series of nested multivariate mixed-effect linear-regression models were investigated.
To determine whether shyness, activity, and exploration were correlated with one another, we built a multivariate mixed-effect model.
Multivariate, mixed‐effect logistic regression model contains fixed effects and random effects.
Multivariate mixed model analyses were performed to identify factors associated with S. aureus colonization.
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