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White-on-white killings and black-on-black killings are nearly twice as likely to occur than a random model projects, at relatively similar levels.
AUC values range from 0.5 to 1.0; with values close to 0.5 corresponding to a model that is no better at predicting an ecological niche than a random model, and a value of 1.0 corresponds to a model with a perfect fit.
The data were interrogated further by a random effects model modelling data at days 30 and 90 simultaneously while adjusting for Hb at day 3 and taking into account random measurement errors and missing data.
One of the most successful approaches to discerning the signal in mutation data or in copy number alteration data is to seek out those genes or regions which are altered at a frequency above a background random model.
Note: CI = Confidence Intervals, ICC = Intraclass correlation for Factor Scores for children within the same household.*All sig at p <.001 A random slope model was used to investigate cross-level interaction; in this case whether household income modified the relationship between obesity and reported emotional or behavioural symptoms.
a Table and forest plot illustrating functional outcome based on DASH scores comparing external fixation with a volar locking plate at 3 months with a random effects model.
b Table and forest plot illustrating functional outcome based on DASH scores comparing external fixation with a volar locking plate at 6 months with a random effects model.
Table and forest plot illustrating radiographic outcome based on volar tilt comparing external fixation with a volar locking plate at 12 months with a random effects model.
c Table and forest plot illustrating functional outcome based on DASH scores comparing external fixation with a volar locking plate at 12 months with a random effects model.
The effects of treatments on adiposity, blood pressure and biochemical indicators were assessed by examining 2-way interactions (time × intervention) with random effects specified in these analyses, which consisted of adjusting errors for clustering at the school level with a random intercept model.
The adopted modelling strategy consisted of increasing model complexity at each step, using a random intercept model (Pornet et al, 2010).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com