The first two columns show heritability estimates using all the samples in each of our two cohorts (both related and unrelated individuals) and are computed using a linear random-effects model and all available genotyped SNPs.
To allow for clustering we used a linear random intercept model with generalised least squares estimates, assuming a Gaussian distribution of the random effects with patients as the cluster variable.
Initial (0 2 years) and steady-state (≥ 2 years) migration were analyzed using a linear mixed model with random slope and random intercept, which accounts for the repeated measurements of migration over time and the correlation of these measurements in patients.
Therefore, any study using a linear amplification protocol with random second round priming, should conduct a pilot study to identify and correct for potential process bias.
We evaluated differences between the two interventions by using a linear mixed model with random nested factors of "patient" and "exercise group".
The first strategy was to estimate the slope of the treatment effects using a linear mixed model, with random effects accounting for the correlation among multiple observations from each subject.
Mean FCU efficiency scores, image quality total score, image quality sub-group scores, and exam duration were compared using a linear mixed effects model with random effects for SP and participant and fixed effects for session and group (GIM faculty, cardiology fellows, and DCSs).
For length of hospital stay we analysed data using a linear regression model with a random hospital effect for the intercept and slope, using the log of length of stay as the dependent variable.
We analyzed within-subject associations between health outcomes and air pollution metrics using a linear mixed-effects model with random intercept, controlling for age, ambient relative humidity, and ambient temperature.
