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Multivariable regression models were used.
Multivariable regression identified patient characteristics associated with intracranial hemorrhage.
Multivariable regression models were constructed to assess trends and factors associated with nonroutine discharge.
Multivariable regression analyses were performed to identify factors independently related to NT-proBNP and BNP levels.
Multivariable regression was used to examine pretreatment characteristics associated with physical function changes.
Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes.
Multivariable regression analyses revealed that depression is an independent predictor of RLS.
Multivariable regression model was fit using the ordinary least squares approach.
Multivariable regression was used to determine trends in ARF among various procedures with CABG as a reference group.
Multivariable regression identified age >75 years, pylorus preservation and operative blood loss >1,500 ml as predictors of SAPO.
Multivariable regression analysis demonstrated a 1.89 increased relative risk of relapse for individuals given IVIg (p = 0.021).
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