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A large number of confounders were evaluated, e.g. current smoking as it reduces the halftime of caffeine with 30 50% [19], and use of oral contraceptives in women because they increase the level of caffeine with as much as 50%[20]].
Potential confounders were evaluated one at a time, based on whether the estimates for the pollutant associations changed by > 10%.
In addition to the main covariates described above, potential dietary confounders were evaluated for inclusion in the Cox regression models.
Potential confounders were evaluated in univariate models adjusted for maternal age at cycle start due to its biological relevance.
The following potential confounders were evaluated: mammography use, medical radiation, lactation, hormone replacement therapy, oral contraceptive use, diethylstilbestrol exposure, body mass index, smoking, alcohol consumption, teen and adult physical activity, race, marital status, and religion.
Other potential confounders were evaluated, including smoking status, alcohol consumption, and annual household income at the time of enrollment, but were excluded from final models because they were not substantially related to MM or family history of cancer.
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The association between dexamethasone treatment, serum lactate levels and possible confounders was evaluated using ANOVA and linear regression.
Confounding was evaluated by entering potential confounders into a logistic regression model sequentially and comparing adjusted and unadjusted ORs.
There were also differences in the confounders that were evaluated in the two studies; in particular, we evaluated a larger number of dietary variables in our models.
In spite of these limitations, we believe they are outweighed by the strengths of this analysis that include the large population size, which provided enough power to detect differences between groups and the wide range of potential and important confounders that were evaluated.
Thus possible confounder effects were evaluated.
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