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We used linear regression to assess the association between each exposure variable and outcome, adjusting for potential confounders.
Linear regression was used to assess the association between each exposure variable and each outcome, adjusted for potential confounders.
We used bivariate logistic regression to estimate the crude associations between each exposure variable and IPV, and to select variables for the multivariate analysis.
We examined whether the association between coffee, black tea, and caffeine consumption and breast cancer survival differed by hormone receptor status, disease stage at diagnosis, or smoking status with a likelihood ratio test comparing the model with the cross-product term between each exposure variable and each potential effect modifier to the model with main effects only.
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We assessed heterogeneity of the HRs across strata of parity by likelihood ratio tests by comparing models with and without product terms between parity and each exposure variable (where exposure categories were treated as a continuous variable).
These relative risks were compared using a χ test for heterogeneity to see whether the effect of each exposure variable varied between tumour types.
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.
The aim of was to find any significant association between an exposure variable and a single inflammatory cell type.
However, this may be an artifact caused by exposure measurement error and high correlation between the exposure variable and the potential confounder [ 6].
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