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After removing a non-significant interaction or variable, a new model was generated and only accepted if the removal did not significantly increase deviance comparing to the previous model after a F test (p>0.05) [37].
Similarly, levels of categorical variables (e.g. admission diagnoses) were allowed to aggregate, if this did not significantly increase deviance of the model.
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Predictors were removed from the model, unless this significantly increased deviance of the model.
According to some authors, early pubertal development could result in affiliation with older adolescents, who often experience increased deviance and substance use [ 27].
The significance of the increase in deviance resulting from the deletion of a variable in the model was estimated using the chi-squared deletion test [44].
Significance among factor levels (e.g. among the 4 different group sizes) was determined by model simplification, where we evaluated whether combining >1 factor level into a single level led to a significant increase in deviance of the model, using F-tests [42].
Any variables that caused an insignificant increase in deviance on removal from the model were left out of the model while the variable that caused a significant increase in deviance on removal was retained in the model.
The significance of each of these variables was estimated by an F-test based on the increase in deviance resulting from its exclusion from the basic model.
Reported p-values of models refer to the increase in deviance when the respective variable was removed ("lm": F-statistics; "glm": Pearson's chi-square).
Reported p-values refer to the increase in deviance in model fit when the respective variable was removed (likelihood-ratio-tests ("lrt")).
We allowed strata of categorical variables (e.g. admission diagnoses) to lump to simpler forms if coefficients of individual levels were similar and aggregation did not increase the deviance of the model.
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