Sentence examples for application of the propensity from inspiring English sources

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

Application of the propensity score in the as-treated and per-protocol analyses could potentially be a novel approach.

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.

In the absence of balanced potential confounders following application of propensity scores, the final effect estimate is prone to residual confounding and, therefore, a better propensity model should be developed.

Additionally, this study will contribute to the growing knowledge of the application of propensity scores, a relatively novel statistical technique to simulate a randomized controlled trial in studies where it is not possible or difficult to execute this kind of design.

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].

The application of propensity weights reduced differences between the two models for all but two patient characteristics (gender, and whether the patient was in a depressive state at the start of the study) and attained standardised differences of less than 0.1, indicating negligible differences [ 31], for five of the twelve characteristics.

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).

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).

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