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Moreover, the multivariable analyses involved detailed and controlled delineation of potential confounding variables.
Multivariable analyses involved adjustment for the following potential confounders; age, BMI, smoking, alcohol consumption, and menopause, relationship and employment status.
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The main secondary analyses involved multivariable logistic regression to adjust for baseline imbalances across the groups of (randomized and consented) women and those characteristics associated with consent.
The sensitivity analyses involved repeating the multivariable modeling excluding those children who did not maintain continuous enrollment in PFK, then excluding those children with less than 60 days of follow-up after their first event, and finally performing the analyses in those children who did not have birth records in the PFK database.
Patients with >450 CD4+ T cell counts/μL serve as the reference group cOther variants are dismissed by univariate and multivariable analyses dIn these 55 patients, DRB1*15 DQB1*06 is the only haplotype involving DRB1*15 In further analyses confined to 229 Hispanics (Table 5), a reduced multivariable model continued to confirm the negative impact of HIV-1 infection on Ab responses (P < 0.0001).
In multivariable analyses, we used 4 models.
The same was true for unpaired and multivariable analyses.
Univariable and multivariable analyses were performed to determine factors contributing to insomnia among individuals with TTH.
Selected variables were further introduced in multivariable analyses using a hierarchical model with two random effects.
However, our findings persisted after adjusting for blood glucose or blood glucose change in multivariable analyses.
We investigated factors contributing to RLS among individuals with TTH using univariable and multivariable analyses.
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