Sentence examples for modeling and propensity from inspiring English sources

To estimate associations between IvIgG administration and mortalities, multivariable logistic regression modeling and propensity score-based matching were used for analysis with SPSS version 22. IvIgG was administered in 976 patients (30.5%).

Should they review curricula and teaching using structural equation modeling and propensity score matching?

This study provided a combination of tests - logistic regression, structural equation modeling and propensity score matching - of the effect of the MPE on ideation and on APB.

Statistical methods used to account for this source of bias include multivariable modeling and propensity scores; the latter can be used to adjust for the likelihood of having received the drug of interest [ 3].

Although a review of the modern methods of causal inference is beyond the scope of this manuscript [ 29– 31], the use of Cox proportional hazard modeling and propensity score analysis to adjust for treatment selection and confounding should be seen as a strong contribution from this manuscript.

To evaluate the effect of antimicrobial strategy on ICU mortality, multivariable Cox model and propensity score analyses were used.

Gender was added to this model and propensity scores were then calculated [ 14].

The multivariate Cox proportional hazards model and propensity score were used to estimate 28-day mortality risk.

A Cox proportional hazards model and propensity score-matched techniques were used to evaluate the effect of treatment on survival.

Treatment group comparisons were based on treatment episodes using 3 statistical approaches (Kaplan-Meier survival analysis, Cox Proportional Hazards regression model, and propensity score-adjusted bootstrap resampling methods).

The impact of preoperative CEA level on overall survival was assessed using risk-adjusted Cox proportional hazard regression models and propensity score methods.