Eight independent patient-, endoscopist- and endoscopy-related variables were analyzed by a multivariate model to determine their association with CP.
Comprehensive serum profiling of cytokines followed by multivariate modeling, similar to that performed in this study of murine colitis, may identify a panel of disease-associated predictive biomarkers, particularly in individuals identified by genetic screening as having an increased risk for IBD.
By restricting our multivariate models to Mexican-American Latinas vs. non-Latinas, the results were unchanged from those including all Latinas.
Thus, by relying on multivariate models to identify high-risk individuals for prevention programs, many false-positive cases would be identified as high-risk individuals and would be invited to participate in the intervention program.
Patient demographics, operative and pathologic findings, and the postoperative course were evaluated both by univariate and multivariate models to determine the impact on overall survival, which was calculated from the time of esophageal resection.
Associations between the variables and the outcomes were estimated by general linear models, first univariately, to identify all associations at p < 0.05, then by step-down multivariate modeling to isolate net effects after adjustment for confounders, with each islet infusion as an independent observation.
Independent predictors of perceived age were identified by multivariate modelling.
The most important cost predictors (including cost drivers) for total, direct, and indirect costs are identified by multivariate modelling approaches.
Univariate logistic regression models, supported by multivariate models, were applied to identify groups of people who had adverse interviewing experiences.
Diabetes mellitus (hazard ratio [HR] 1.63 [95% confidence interval (CI) 1.17 to 2.28], p = 0.004) was an independent predictor of absent myocardial perfusion (MBG 0/1) and absent STR (HR 2.94 [95% CI 1.64 to 5.37], p = 0.005) by multivariate modeling.
