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Non-linearity was tested by adding quadratic terms of subsequent conditional BMI SDS scores to the models (at a type 1 error rate of 0.05).
Linearity was assessed by adding quadratic terms to the models and examining generalized additive models.
Linearity assumptions were relaxed for continuous variables (gestational age and mother's age) by adding quadratic terms to the model.
We tested for deviations from log linearity by adding quadratic terms to the continuous models and by adding separate parameters for individuals in the lowest and highest deciles (threshold effects).
Finally, the preferred linear model was tested for non-linearity, by adding quadratic terms for exposure covariables, and time or age effect modification (ie, adding h a, t) functions to the model).
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Tests for adding quadratic terms for the continuous SES measure are not significant, and likelihood ratio tests for intraclass correlation coefficients > 0 are significant in all models, indicating that there is detectable clustering by census district.
[11] We investigated model misspecification by examining the significance of added quadratic terms using the Wald test, and we assessed model calibration using the Hosmer-Lemeshow test.
We added quadratic terms of salt intake to our models to statistically examine potential non-linearity.
Between the two linear regression models, the quadratic model performs better than the interaction model, potentially due to the added quadratic terms, suggesting the nonlinearity of the response of these cells to the drug combinations used.
We assessed departure from linearity graphically and by adding quadratic and cubic terms into the model.
Nonlinearity in adiponectin trajectories was checked by adding quadratic and cubic terms of time-by-caseness interaction to the models; they were all nonsignificant (P > 0.1); thus, we describe trajectories only with linear time terms.
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