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Here, in equivalent bivariate analyses we identified peaks in low LS at age 40 54 years in both deprived and middle tertiles but at age 55 64 years in the most affluent.
From bivariate analyses, we identified six variables characterizing CWS: thoracic pain (neither retrosternal nor oppressive), stabbing, well localized pain, no history of coronary heart disease, absence of general practitioner's concern, and pain reproducible by palpation.
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Through bivariate association analyses, we identified the SOX6 gene (SRY-box 6) as a potential pleiotropic gene underlying both obesity and osteoporosis.
Through bivariate association analyses, we identified two distinct biomarker profiles associated with OSA in subjects with NGM and IGM.
For bivariate analyses, we used linear regression.
For bivariate analyses, we used χ tests for categorical variables.
For bivariate analyses, we compared weighted proportions using the Wald test.
In bivariate analyses, we examined the relationship between phthalate and phenol biomarkers and maternal characteristics.
These techniques were also used for bivariate analyses that identified factors associated with study dropout before disclosure of genetic risk information.
We used bivariate analyses to identify variables potentially predicting the presentation of MI.
We used bivariate analyses to identify differences in the receipt of preventive health services, including: cervical cancer screening, mammography, STI testing, safe sex counseling, influenza vaccination, TB testing, mental health screening, and substance abuse screening.
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