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Since RRT was not randomly assigned in our study population, treatment selection bias was accounted for by using the propensity technique.
However, by applying the propensity technique and matching on age, and center and period of admission, we dealt with confounding more extensively than in prior reports.
The aim of this study was to use the propensity technique to estimate the association of RRT with in-hospital mortality in ICU patients with AKI.
By using the propensity technique, we were able to compare hospital mortality rates in matched patients with and without RRT, having a close probability of receiving RRT (somewhat as though RRT had been 'randomly assigned').
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Using data from Nanjing, China, this paper applies a cluster analysis method to characterize three distinct types of residential built environment and applies the propensity score technique to control for self-selection when examining the travel behavior effects of the three built environment types.
We employed the propensity score technique to adjust for those factors.
A propensity score technique was used to balance covariates associated with antimicrobial therapy between groups [ 21- 23].
Therefore, nearest-neighbor matching of 1,686 non-physicians to the 1,686 physicians was performed by propensity score technique [ 13].
A propensity scoring technique [ 12] (using an SPSS add-in a wadd-in autilized in this analysis.
The propensity score technique described by Rosenbaum and Rubin is a powerful method to control for treatment selection bias [ 8, 9].
A propensity score technique was used to balance covariates associated with ACE inhibitor use between groups [ 18, 19].
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