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The association of headache and monthly headache frequencies with PSG subgroups was assessed using multivariate logistic and ordered logistic regression analysis.
We used multivariate logistic and linear regression analysis to examine the association of implementation phase with mortality and LOS after adjustment to propensity scores.
Multivariate logistic and linear regression modeling were used to assess whether differences in clinical characteristics remained significantly different upon controlling for demographic factors, including gender, education, marital status, occupational status, and clinical factors, including bipolar subtype and onset age.
Multivariate logistic and linear regressions were used to evaluate the degree to which treatment intensity predicted 3-month and 12-month outcomes (anxiety sensitivity, phobic avoidance, depressive symptoms, disability, and medical and mental health functioning) after controlling for potential confounding baseline variables.
By using multivariate logistic and stepwise regression analysis, the 9-SNP panel was examined in combination with the pre-stipulated clinical variables above.
Using multivariate logistic and stepwise regression analysis from run 1, the SNPs were examined along with relevant non-genetic factors which identified age and family history of lung cancer as significant contributors to lung cancer susceptibility.
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Parameters were compared using univariate and multivariate logistic regression and chi-square tests.
Multivariate logistic regression and Receiver-operating characteristic (ROC) curves were generated.
Multivariate logistic regression and Cox regression models were used for analysis.
Multivariate logistic regression and thematic analysis of qualitative interviews (N = 149) reveal a nuanced picture.
Multivariate logistic regression and 95% confidence intervals were applied to estimate the risk for UC.
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