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In the multivariable analysis, stepwise selection was used to control for variables with p values < 0.10 for model entry and > 0.05 for removal.
Risk factors with a significance of P < 0.10 in the univariate analysis were entered manually into the multivariable model using stepwise selection.
The multivariable analyses used a stepwise selection with removal of covariates when p > 0.1.
Covariates with p ≤ 0.10 from an exact conditional score test in univariable models were examined together in a multivariable model using the stepwise selection method.
In multivariable regression analyses with stepwise selection allowing for univariately and clinically significant correlates of autonomic neuropathy, updated mean HbA1c was not significantly associated with autonomic neuropathy.
For CDSP, multivariable regression analyses with stepwise selection, allowing for univariately and clinically significant correlates of CDSP, revealed that updated mean HbA1c was not significantly associated with CDSP; however, each SD change in SIF was associated with a 2.7-greater likelihood of CDSP.
Variables were selected for the multivariable survival analyses by backward stepwise selection, with removal testing based on the probability of the likelihood-ratio statistic, at a P > 0.10.
Multivariable linear regression with forward stepwise selection of variables.
The prediction model was developed with multivariable logistic regression with backward stepwise selection.
Variables with P <.1 in univariable analyses were tested by stepwise selection in multivariable models.
We assessed HIV infection's independent association with HOMA by forward stepwise selection in multivariable linear regression models that adjusted for age, BMI and WHR (table 3).
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