Sentence examples for the application of propensity from inspiring English sources

As the application of propensity score matching requires a binary treatment assignment, we cannot directly use the intensity treatment in our PSM.

As matched employer employee survey have a complex sampling design, we rely on the recent literature regarding the application of propensity score matching to complex surveys (Austin et al. 2016; DuGoff et al. 2014; Zanutto 2006).

However, because the application of propensity score methods for continuous exposures is less well developed than their use for binary exposures, we adjusted for potential confounders using multivariable logistic regression models [35].

There are many types of matching estimators, among others inverse-probability weighting, kernel matching, nearest neighbour matching.15 As the data used here have a complex survey design, we rely on the recent literature regarding the application of propensity score matching methods to complex surveys (Austin et al. 2016; DuGoff et al. 2014; Zanutto 2006).

Therefore, to address these analytic requirements, we detail the application of propensity score weighting to examine the causal effect of rectal bacterial STI on HIV acquisition [ 8].

In addition, this study will also reveal new knowledge on the application of propensity scores, a novel statistical tool to simulate RCTs in situations in which a classical RCT is not feasible.

Since then, a variety of propensity score techniques have been developed for both the estimation and the application of the propensity score.

To verify the covariate balancing after the propensity score matching, the standardised difference before and after the application of the propensity score matching was calculated.

To compare the balance between the groups after the application of the propensity score, the means and standard deviations for age and length of hospital stay for the stroke patients who did and did not receive acupuncture treatment were compared using t-tests.

We used the non-parametric method involving the application of a binary propensity score matching estimator.

Balance is most often examined using standardized bias, calculated as the difference in mean covariate value between exposure groups, divided by the standard deviation of the covariate in the entire study sample following application of propensity scores [ 8].