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Additionally, one big multiple model was fitted, including all variables.

The resulting multiple model was the main model with regard to this dependent variable.

In addition, the final multiple model was also adjusted for age and BMI, as these factors were identified as confounders during the purposeful selection process.

Inclusion of variables in selection for the multiple model was hierarchical [ 33]: distal level – information relating to the profile of child and mother; intermediate level – present family environment; and proximal level – maternal conditions during pregnancy and childbirth.

The multiple model was built by entering those variables that had univariate statistical significance with a p < 0.05 in the correlation, retaining those variables with p < 0.05 in the final regression model.

Using the validation database, the AUC for the final multiple model was 0.69 (95%% CI 0.66 0.72), which was identical to the AUC retrieved from a model not including 2-h glucose (0.69, 95%% CI 0.66 0.72), and larger than from a model including 2-h glucose but not BMI (0.63, 95%% CI 0.60 0.67).

With respect to classic used multiple models, the decoupled multiple model is characterized by heterogeneous submodels in the sense that their state spaces may be of various dimensions.

A particular class of multiple model, known as decoupled multiple model, is used in order to cope with the state estimation problem of nonlinear systems.

In [15], interacting multiple model is proposed for moving target location estimation in cellular network.

Significant variables from univariate models not being significant in the big multiple model were excluded.

All significant variables from the univariate models and the big multiple model were selected for a final model.