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The precision-weighted meta-regression models incorporated random effects by using a restricted maximum likelihood method to estimate the among-study variance (20 ).
All models incorporated random effects for clustering in the data due to randomisation by general practice and for the correlation due to the repeated measurements for each individual across time.
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Models incorporated a random intercept for the population and random slopes for the linear and quadratic time effects.
In addition, the GLMM models incorporated random-effects for hospitals to adjust for the clustering of admissions within hospitals.
These models incorporated a random intercept term to account for the correlation among repeated measures.
Furthermore, only one model [38] incorporated random fluctuation in CD4 (or RNA) measurement in simulations.
The study-specific adjusted odds ratios and standard errors were combined in a second-stage linear mixed-effects model that incorporated random study effects (inter-study heterogeneity) to produce a pooled exposure odds ratio and standard error.
First, we incorporated random effects into linear models to account for within-animal autocorrelation due to repeated measures.
These models incorporated the structure of the data by including random effects for person, practice and year.
To do so, we incorporated random effects by rat (random intercepts and slopes) into linear models using PROC MIXED.
The models incorporated CpG sites that were differentially methylated at the 0.001 significance level as assessed by the random variance t-test [23].
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