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All significant factors on univariate analysis were considered for inclusion in the multivariable logistic model.
Then, a multilevel multivariable logistic model was built with centers treated as random effect.
Both variables remained significant predictors in the multivariable logistic model (Table 3).
The number of cattle herds inside a 500 m wide buffer surrounding all land parcels belonging to a farm remained the only significant predictor in the multivariable logistic model of the probability of a confirmed bTB herd breakdown.
The number of M. bovis positive badgers that were culled outside but within 500 m of the land parcels belonging to a farm remained the only significant predictor in the multivariable logistic model.
The multivariable logistic model was obtained using backward elimination.
Similar(5)
Numerical qualities were assessed for variables to be used in multivariable logistic models.
Multivariable logistic models were used to determine association between hydrologic and soil-hydrologic variables and test status.
Results from multivariable logistic models are both corroborative and revealing.
Correlates of insufficient PA were assessed by multivariable logistic modeling.
Otherwise, adjusted ORs were estimated by multivariable logistic models.
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