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The linear mixed model included random intercepts for subject, visits, and circular test pattern [10].
The linear mixed model included random intercepts for subject, visits, eye, and circular test pattern [10].
The model included random effects for the general practice.
The model included random intercepts and random slopes for time.
The first model included random intercepts for subjects and items only.
The model included random intercepts to account for the variability of the outcome between receiving countries.
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In line with this, Setzer et al. (2013) analyzed binary solution-behavior indicators by means of a hierarchical generalized linear model including random intercepts for person and institution (but without random item intercepts or explanatory person and item variables).
In each of our analyses, we started with a base model including random per-participant and per-item variation.
Model includes random effects for patient and infusion within patient.
Adding FIP+FIP2 increased the multiple R to 69.9%, with the full model including random effects of batch, chamber and batch×chamber (but omitting FIP3 and FIP4) explaining 76.6%.
A mixed model including random effects for slide and animal was fit to the expression data for each gene using GLM procedure in SAS.
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