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As exploratory analyses, the role of genotypic effects across various high-risk subgroups was examined both with stratified analyses and by fitting interaction terms into the regression models.
Effect modification by sex was explored by fitting interaction terms into the full model.
Before carrying out specific subgroup analysis, we will identify differential effects by fitting interaction terms.
Effect modification by sex was examined in stratified analyses as well as by fitting interaction terms.
The possibility of the intervention effect being modified by covariates was assessed by fitting interaction terms into the linear regression models.
First, we examined effect modification by age, prior coronary heart disease, and smoking status by fitting interaction terms in the single pollutant models.
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To explore possible interactions we fitted interaction terms (model 2) separately for each drug or drug class by age (>65 or ≤65), sex, aspirin use, and statin or fibrate use.
Gender differences were tested by fitting interactions.
We also analyzed the interaction between mtDNA haplogroups and age, and between mtDNA haplogroup and APOE by fitting the interaction terms "age by mtDNA haplogroup" and " APOE genotype by mtDNA haplogroup" into the model.
Tests for differences in the absolute treatment effect according to each characteristic (ie, tests for interaction) will be derived from standard tests for heterogeneity/trend across subgroups or, where needed, by fitting appropriate interaction terms with treatment allocation in a linear regression model.
In addition, we tested for the moderating effect of relevant covariates by fitting appropriate interaction terms in the model.
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