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We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km.
After describing the relationships between explanatory variables, we built a multivariate model using logistic Generalized Linear Models (GLMs) and estimated parameters with a Generalized Estimating Equations (GEE) procedure.
For each tool impact score we built a multivariate model using doctor characteristics as predictors.
In step 2, we built a multivariate model for the nine subscales of the RAND 36, using a backward stepwise procedure, because the subscales are highly correlated.
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These variables were then selected in a multivariate logistic regression via a stepwise procedure to build a multivariate model designed to identify factors independently and significantly associated with good neurological outcome.
There were insufficient data to build a multivariate model.
Logistic regression was used to build a multivariate model of predictors of PSA testing.
Univariate associations with a p-value < 0.25 were used to build a multivariate model using a backward elimination method.
To build a multivariate model, we used forward selection to evaluate the additive effects of risk factors.
To build a multivariate model predicting axillary LN involvement, a logistic regression with backward procedure including intra- and peritumoural 'vascular' invasion, LVI and BVI was performed.
Stepwise backwards least-squares linear regression (PROC GLMSELECT, SAS 9.3, Inst. Inc., 2011) was used to build a multivariate model with a constant sample size of 233 farms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com