The phrase "adjusted for correlates" is correct and usable in written English.
It can be used in contexts where statistical analysis or research is being discussed, particularly when accounting for variables that may influence the results.
Example: "The results were adjusted for correlates such as age, income, and education level to ensure accuracy."
Alternatives: "controlled for variables" or "accounted for factors".
Individuals with RLS were more likely to have certain functional status limitations at baseline (Table 4, adjusted for correlates reported in Table 3; specifically age, gender, moderate/severe pain, drinking status, BMI, health limiting ability to work, previous falls, other sleep symptoms, income, out-of-pocket medical expenses, frequency of provider visits, and number of chronic medications).
Next, a series of logistic regression models was conducted to assess the associations between behavioral and affective factors and SUD adjusting for correlates from other domains.
Perceived neighborhood safety was an important predictor of depressive symptoms in models that adjusted for other correlates of depression.
Estimates are presented from models that mutually adjusted for all correlates considered along with 95% confidence intervals (CI).
In the multivariate Cox proportional hazards regression models, waist girth lost the most of its predictiveness when the models were adjusted for variables correlating with waist girth.
Multivariate analyses for each correlate were adjusted for all other correlates and last method of contraception.
Although we adjusted for most recognized correlates of poor maternal mental health, violence and low birth-weight, the possibility of residual confounding cannot be excluded.
RLS was then considered as an independent variable, adjusting for significant correlates from the RLS prediction model, to predict concurrent functional limitations.
Interestingly the associations between age, employment and leisure time physical activity were strengthened or became significant after adjusting for other correlates.
We fit a linear regression model, adjusting for baseline correlates of maternal HIV infection status, to determine differences in baseline CES-D scores between HIV-infected and -uninfected pregnant women.
Such studies will also need to adjust for other correlates of depression, in addition to maternal age and marital status, such as food insecurity and women's access to different forms of social support [ 31].
