In the full model, adjustments did not change the HR (HR 0.75, 95% CI 0.71, 0.80).
When we additionally adjusted for the other six beverages in the multivariate regression model, this mutual adjustment did not change risk estimates for each specific alcoholic beverage (Model 2), suggesting that strong beer, white wine, and red wine were each responsible for the reduction in risk of renal cell cancer.
†Adjusted for age in two year intervals (model with full adjustment did not converge).
This adjustment was preferable to adding age as a covariate in the model because the adjustment did not assume a linear relation between the HAZ and age.
In the present study, BMI gain was included into a logistic regression model; however, the adjustment did not diminish the effect of childhood BMI for the prediction of metabolic syndrome.
For negative controls, we performed statistical analysis with the HGU133A normalization control probe sets using the same ANCOVA models, ensuring the adjustment did not produce "noise" or aberrant false positives.
The model simulations with this adjustment did not significantly overestimate the total number of coastal fadeouts (figure 4b and 4c).
Finally, we tested for the effects of clustering by neighborhood, using random effects models, but because such adjustment did not alter effects, results without adjustment for clustering were provided.
In multivariate model, adjustment was done for age, height, weight, sex, smoking status, spoken dialect, education, socio-economic status (SES) and arsenic levels in drinking water.
Model-like adjustments did not decrease HRs significantly (Table 2).
