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The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities.
In the multivariate model using adjusted charges across hospitals as the dependent variable, hospitals with for-profit ownership, severe casemixes and high wage indices charged significantly more than their counterparts for uncomplicated vaginal delivery (table 3).
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Cancer screening, ART prescribing and healthcare utilisation across models using adjusted multivariable hierarchical logistic regression analyses.
These models use adjust for age using the continuous measure.
Accounting for the different number of predictors included in each model by using adjusted R2 values for the variance partitioning analyses produced very similar results (see Supplementary Table 3b).
We validated the estimated first order polynomial regression meta-model using adjusted R2 and Relative average absolute error, Our results demonstrate that As a result, OLSR is more efficient and more validation than GLS method.
Unconditional logistic regression in successive models was used, adjusted for socio-demographic variables and other confounders.
As a measure of outcome in logistic regression models, we used adjusted ORs (aORs) and 95% confidence intervals (CI).
Analysis of covariance models with baseline clinical measurement group as the main effects in the model were used, adjusting for age and gender.
Because cancer detection rates are affected by other factors, a logistic model was used, adjusting by radiology unit, initial and successive screening, and age.
To account for multiple OOA subjects who were recruited from the same household, a mixed model was used, adjusting for family as a random effect.
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