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This limitation can be mitigated by using multivariate linear, non-linear and polynomial regression analyses of RMR input parameters.
We examined the effects of fetal growth velocity, post natal growth and birth weight on subcutaneous fat mass measured at 4 and 12 months of age, by using multivariate linear models.
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The associations between the PAV-COM score and behaviors were stratified by survey year and tested for significance using multivariate linear regression models adjusting for age, gender, and race/ethnicity.
This is supported by the results of the secondary analysis using multivariate linear regression, which showed that the number of units transfused was associated with the presence of acute renal failure requiring dialysis.
Associations between migraine frequency and RLS prevalence were investigated using multivariate linear and logistic regression.
Variables associated with work ability were evaluated using multivariate linear regression analyses.
We identified relevant behavioral and spatial variables that affect fecundity rates using multivariate linear regression models.
Data were analyzed using multivariate linear regression models.
Continuous BMI z-scores were analyzed using multivariate linear regression.
Relationships were analyzed using multivariate linear and logistic regression models.
The hypotheses will be tested using multivariate linear regression analyses.
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