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The strategy for building multivariable models was as follows.
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When simultaneously evaluating multiple predictors for disease outcome, current methods focus on building multivariable models, rather than the evaluation of single exposures adjusting for known covariates [ 15].
The GBM modeling approach is a machine learning technique for building a multivariable prediction model by incorporating all of the variables without variable selection.
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When building the multivariable models, a number of decisions were made for practical purposes.
There is no consensus on the optimal approach to building a multivariable predictive model [ 27].
This sample was determined with the primary goal of building a multivariable regression model to compare the overall attitudes among the seven groups and the general public controlling for several demographic and other confounding variables.
It is an important first step in building a multivariable model, which can be externally validated in future studies.
Building the multivariable model began with inclusion of key variables based on a priori hypotheses (i.e., prior FQ use).
The badger-related, sett-related and herd-related variables which demonstrated the most significant univariable associations with time to first confirmed bTB herd breakdown were then retained for multivariable model building controlling for local farm-level risks [Supplementary Information S1].
The badger-related, sett-related and herd-related variables which demonstrated the most significant univariable associations with the risk of confirmed herd breakdowns were retained for multivariable model building.
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