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Interactions between predictive variables were not further considered.
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Most predictive variables were categorized a priori.
The predictive variables were the above described objective, socioeconomic, subjective and treatment variables.
The primary predictive variable is the change in ADHD symptoms between t0 and t2, determined by the difference in scores on the ADHD RS between t0 and t2.
The primary predictive variable was the mode of transport.
The primary predictive variable was maternal race/ethnicity.
Multivariate Cox proportional hazards regression models were used to estimate the association between predictive variables (those variables that were associated with the outcome in bivariate analyses with P <0.10) and premature AI discontinuation.
As demonstrated in this paper, the method is adaptive in automatically being able to include both linear and nonlinear dependencies, after determining which is appropriate, and it can include interactive dependencies between predictive variables – an example of the latter was shown for the variables of humidity and temperature.
Study objectives: Bayesian networks analyze interdependencies between predictive variables.
Prior to running predictive models, correlations between predictor variables were assessed to test for collinearity using correlation matrices and simple linear models generated for each pairwise combination of predictors.
Two way interactions between variables were considered.
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