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All forms of two-factor interactions, as well as three-factor interactions between the three predictors were included in the model.
Figure 1 illustrates the interplay between the three predictors.
We then go on to describe the differences between the three predictors.
Next, we conducted multivariable logistic regression to find out if multicollinearity or synergism existed between the three predictors (HIV, age, and duration of sexual life).
Within each cross-validation step, variable selection between the three predictors was performed using best subset selection with the AIC criterion (Akaike's An Information Criterion).
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Despite the large overlap between disorders (comorbidity), none of the interaction-terms between the three predictor-variables (LBP, headache and asthma) were found to be statistically significant, and therefore the multivariate models were constructed without inclusion of interactions.
The largest sample of 1639 individuals, used in assessing the interaction between the 10-SNP set and Parent Negativity at age 9, had 80% power to detect a total association between the mathematics composite score and the three predictors of 0.66% effect size, and with one individual predictor of 0.47% effect size.
Moreover, our analysis revealed that the phase shift between the two predictors was significant (Supplementary Fig. S4).
Activity across the entire striatal ROI was significantly associated with unique MS-PE (t(18)=4.172, P<0.001, Cohen's d=0.957) but not with unique ERO-PE (t(18)=1.162, P=0.260, Cohen's d=0.266), although the difference between the two predictors did not reach significance (t(18)=1.558, P=0.137, Cohen's d=0.357).
The overall sensitivity and specificity over the predictions is nearly the same between the two predictors.
To the extent that nutrients and SST are correlated, the statistical model is unable to distinguish the effects of one predictor from the other; the chlorophyll response is divided between the two predictors.
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between the three measurements
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