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A test for co-linearity between the exposure variables was also completed using variance inflation factor (VIF) larger than 10 as evidence of co-linearity between two exposure variables.
Correlations between the exposure variables were calculated with Pearson correlation coefficients (r).
To find association between the exposure variables and adherence, bivariate analysis was done.
Mixed model logistic regression was used to assess the association between the exposure variables and the pregnancy outcomes.
χ Test-for-linear trend was used to assess dose response relationships between the exposure variables and DLE.
Chi-square tests for linear trend were used to assess dose-response relationships between the exposure variables and DLE.
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We first examined the univariate relationship between all the exposure variables and mental health symptoms.
We investigated possible interactions between the exposure variable and plausible confounders.
GEE models included an interaction term between the exposure variable and child age at examination.
Similarly, in linear regression, a linear relation is assumed between the exposure variable and the response variable.
However, this may be an artifact caused by exposure measurement error and high correlation between the exposure variable and the potential confounder [ 6].
More suggestions(13)
between the factor variables
between the exposure groups
between the exposure conditions
between the exposure spots
between the exposure rates
between the exposure zones
between the exposure components
between the exposure predictors
between the exposure metrics
between the exposure periods
between the exposure biomarkers
between the exposure variable
between the explanatory variables
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