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To improve model fit, outcome variables were natural log transformed and then the regression coefficients were back transformed to original scale to ease interpretation.
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Delay of delivery was consistently reported and thus the model fits this outcome best.
Inclusion of covariates in multivariable models was based on a priori considerations of covariate associations with exposure and outcome, model fit (statistically significant partial F-test at α < 0.10), and whether covariate inclusion materially affected the organochlorine exposure effect estimate.
All the four regression models had similar AICs, including the model fitted for the Outcome 1 (despite having the smallest number of complete observations).
The model fit was adequate for all outcomes.
The model fit was good for all outcomes (see web extra appendix 2).
Consequently, G2 was of no use, and therefore for assessing GRM model fit we focused on other outcomes.
The test of model fit suggests that the relationship between outcome and ED visits was linear.
As an additional measure of model fit and ability to predict an outcome the ROC curves [ 21] were drawn.
Adding the FSIQ×time interaction (χ 2 (1) = 8.63, p < .01) did increase model fit, indicating that FSIQ moderated the outcome in CGAS scores.
Adding the PIQ×time interaction (χ 2 (1) = 5.01, p < .05) did increase model fit, indicating that PIQ moderated the outcome in HoNOSCA scores.
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