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Tests for interaction (effect modification) between covariates were performed with the Wald test statistic.
Tests for interaction (effect modification) between covariates were performed for one pair of covariates at a time using the Wald test statistic.
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We further tested for effect modification between isoflavone intake and other covariates (age, sex, cigarette smoking, alcohol drinking and body mass index) in colorectal adenoma through the addition of multiplicative interaction terms into the model, but found no statistically significant P-value for interaction (data not shown).
Possible between-covariate interactions were considered.
The modifications of ORs between univariate and multiple conditional logistic regression analysis were due to effect modification by covariates and not because of change in selection of individuals when restricting to those with complete data sets (data not shown).
Correlation between covariates was assessed to investigate collinearity.
Final analysis was performed between covariates reaching a significant p value.
Binary logistic regression analysis was undertaken to identify any association between covariates and outcome (PVA).
Interaction between covariates was examined by scatter plot of covariate values and change of OFV between models with single or combined covariates.
We included interaction terms to evaluate effect modification by covariates.
Possible interactions between covariates were also evaluated.
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