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Because DEA is very sensitive with regard to missing data and outliers, we pursued two strategies to adjust for the missing vaccination services at three CSPS: (1) Vaccination input and output data were balanced according to the catchment area.
All variables included in the multivariable analysis were checked for co-linearity and that data were balanced.
Unbalanced data were balanced to an interval of 4 weeks, and missingness completely at random was assumed, and a sensitivity analysis assuming missingness at random was also performed.
In most cases, these injuries were similar in terms of severity of local and general trauma, so overall data were balanced.
Again, data were balanced at baseline with no statistically significant differences between the TG and the PG, regarding classification of sarcopenia (TG=eleven class I and fifteen class II; PG=ten class I and seventeen class II).
One hundred and eighteen patients (69.8%) were assessable for analysis of subsequent treatments, whereby available data were balanced between the low and high baseline CEA groups (58/118 [49.15%] and 60/118 [50.85%], respectively).
Similar(49)
Inference 137, 1199 1212] when the data are balanced.
When we train our models, we make sure the data is balanced".
This is an appropriate metric as the data is balanced and both classes are of equal importance.
Because the data are balanced (four replicates for each case study) and the factors are fixed, the two-way analysis of variance was applied.
These apparently conflicting data are balanced by those emerging from the "assessment of the agreement among the referents" reported in Table 2.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com