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In the final model, we controlled for the clinically relevant risk factors regardless of their statistical association (i.e. gender, secondhand smoke exposure at home, allergic disease in both parents), and those factors correlated with both the bedroom concentration and the outcomes.
Using a multivariate logistic regression model, we controlled for several risk factors.
In this model, we controlled for time and other socio-demographic characteristics.
In the multivariate model, we controlled for the demographic factors of age and study site.
In the multivariable logistic regression model, we controlled for a priori confounders; age group, gender and area income deprivation.
In this regression model we controlled for age, gender, duration of diabetes, baseline HbA1c value and occupational activity.
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As we control for the lagged terms in the segmented work history model, we control for heterogeneity.
In all three models, we controlled for the sociodemographic variables age, gender, educational attainment,8 and migration status.
In the basic models we controlled for trial arm, maternal and paternal age and child's sex and age.
In addition to accounting for phylogenetic relatedness in our mixed-effects models, we controlled statistically for potential spatial autocorrelation among the species examined (see Supplementary Tables S9 10).
In further models, we controlled for lifestyle factors and BMI.
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