In-hospital mortality and midterm survival were analysed using mixed effects logistic, and stratified Cox proportional hazards regression models respectively.
Both analyses were performed using mixed effects linear and generalized models.
Significant differential gene expression levels were detected using mixed effects modeling and Statistical Analysis of Microarray (SAM).
Lastly, the relationship between all health outcomes was examined using mixed effects logistic and linear regression to account for clustering, adjusted for gender, country of birth, ethnicity, borough, parental employment, family affluence, and all neighbourhood characteristics.
Where necessary in the analysis, the longitudinal and hierarchical/cluster nature of the study design will be taken into account using mixed effects models and other appropriate methods (see below).
The longitudinal submodel was fitted using mixed effects logistic regression, and the survival submodel was a Cox model adjusted for age, gender, severity of disease (SAPS II, KDIGO grade) and use of life support (norepinephrine, mechanical ventilation and dialysis).
For each analyte, intraclass correlation coefficients (ICCs) and 95% CIs of log-transformed baseline, sample 2, and sample 3 values were estimated using mixed effects models with batch and participant included as random effects.
The resultant RW values were analyzed using mixed effects ANCOVAs with cross, treatment and cross × treatment interactions as the model terms, centroid size (a measure of allometric variation in body shape) as the covariate, and tank as a random effect.
Similarly, Botton et al. (2008) using the adapted Jenns-Bayley model and using mixed effects modelling, also found increased risk of obesity due to rapid growth in the first 6 months in French children.
The first step in generating age- and species-specific prevalence data at all schools in coastal and western regions involved modeling the relationship between infection status and age, using mixed effects logistic regression.
