Sentence examples for controlling for propensity from inspiring English sources

Despite these limitations, the coefficient for ultrasound receipt was still significantly different from zero (P = 0.032) when controlling for propensity score quartile.> -wrap-foot> Logistic regression analysis of propensity score quartiles and use of real-time ultrasound guidance as predictors of any complications of tracheostomy.

All adjusted proportions and Cox-regression models were controlled for propensity score as well as several additional prespecified covariates.

We will test for differences in rates using fixed effects conditional Poisson regression models, controlling for the propensity score and residual imbalances among important covariates.

Third, despite controlling for race in propensity scores, it is likely that we were unable to control for all possible race/ethnicity effects due to limitations in the Medicare race/ethnicity data (McBean 2004).

The first Cox proportional hazard model assessed the association between receipt of nafcillin or cefazolin versus vancomycin and mortality controlling for the first propensity score.

The second Cox proportional hazard model assessed the association between switching from vancomycin to nafcillin or cefazolin versus remaining on vancomycin and mortality controlling for the second propensity score.

These analyses will also control for the propensity to receive an initial prescription.

Assessments were conducted and measured uniformly between treatment groups, and through control of the baseline HRQoL covariates and use of the random-effects model, the analysis incorporated and controlled for the propensity to respond in a certain way.

We assessed ad exposure to non-tobacco products to control for the propensity to be receptive or attentive to advertising in general, which could confound the relation between tobacco-specific advertising exposure and smoking behaviour.

To improve comparability of the 2 vaccine groups, we controlled for the propensity score in multivariate models, but first we excluded all outlier observations ("trimming"), defined as those below the lower 2.5% of the tail of the TIV observations (3,355 observations) and above the upper 2.5% tail of the ATIV observations (2,894 observations).

The paper gets a related result — about the value of the top pick — by controlling for what economists call "propensity scores," something the researchers encountered in their graduate work.