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Multivariable regression was used to adjust the association between ADLs and QOL for important covariates.
Finally, we used logistic regression models to adjust the association between the subscale scores and mammogram utilization.
Multivariable linear regression was then used to simultaneously adjust the association of the different covariates with SAR and SMR.
A multivariate logistic regression analysis was used to adjust the association between SC implementation and outcomes for confounding factors.
The BMI (calculated as weight/height) was used to adjust the association between blood lipid and physical fitness parameters.
We did adjust the association for BMI, lifestyle, occupation, family history of diabetes, and depression, suggesting that these may not explain the associations.
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Both demographic characteristics and lifestyle variables were used to adjust the associations between perceived walkability and self-rated health.
The food intake data (total energy intake in particular) were used primarily to adjust the associations of the physical fitness and blood lipid parameters.
Multiple linear regression models were applied to adjust the associations between CML and IENFD and between HbA1c and IENFD for age, height, and BMI and to investigate the simultaneous associations among hydroimidazolone, HbA1c, and heat-pain threshold in the leg.
In cases when a statistically significant association between immune cells and organochlorine compound exposure was found in simple regression analysis, multiple regression analysis was used to adjust the associations for potential confounders.
Regression modelling is commonly used to account for confounding by adjusting the association of interest for the effect of other variables associated with the outcome and the exposure variable.
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