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Lastly, we examined the independent effects of these associations by fitting a multivariable logistic regression model.
We began by fitting a multivariable model including the sociodemographic variables alone, testing for evidence of important interactions.
By fitting a multivariable Cox regression, variables independently associated with the endpoint were consistent with those obtained for the subset of treatment-naïve patients (data not shown).
The endpoint stem revision for any reason was further investigated by fitting a multivariable Cox regression model including the covariates HA coating, age, sex, and diagnosis.
For each patient, the propensity score was estimated by fitting a multivariable logistic regression of statin exposure (yes = 1, no = 0) on all baseline covariates together with the first terms interactions and age to the square.
We also explored the possibility of a nonlinear relationship of heme iron intake with GDM risk by fitting a multivariable logistic regression model that implemented the generalized additive modeling method (20).
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32 To control for confounding by indication, we fit a multivariable logistic regression model, from which we estimated a relative risk and 95% confidence interval for in-hospital mortality.
We fitted a multivariable log-binomial model, using GEE to account for clustering by ward, to estimate RR after adjusting for potential confounders.
Stepwise forward selection was used to fit a multivariable model.
We used a risk-factor modeling approach to fit a multivariable model.
First, clinical data only were selected to fit a multivariable model.
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