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The statistical models included an area-level measure of diagnosis propensity to control for these effects.
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We used propensity scoring to control for selection bias.
Regression analyses of costs and depression outcomes were conducted, using propensity weighting to control for potential confounders.
However, even after using propensity scores to control for confounding by indication, amputation group still had higher mortality.
The elderly group (n = 523) to the non-elderly group (n = 20379) approximate ratio of 1 39 allowed us to calculate propensity scores to control for demographic variables.
There is also potential to replace RCTs with analysis of routine data, using techniques such as instrumental variables and propensity scores to control for bias [ 5].
We also considered Deyo-Charlson Comorbidity Index (CCI) score and propensity score to control confounding and reduce bias in the background covariates between both cohorts in studies using healthcare administrative databases [ 28– 30].
Patients who were younger than 65 years (n = 507) were pair-matched with those who were 65 years and older (n = 507) with the use of propensity scores to control for confounding bias.
Strengths of this study are the new user design and use of propensity scores to control for available risk factors, including demographic factors, history of respirator, cardiovascular and other illness and respiratory, cardiovascular and other medications.
Similar to previous research in the area [ 21], the study was also unable to include a non-exposed group, due to the extent of the violence within the Quechua population in this region, which precluded the employment of techniques such as propensity scores to control for confounding variables.
In both models, there were no differences between patients with and those without RRT in biological parameters likely to trigger RRT on reaching maximum RIFLE class (Table 5), thus confirming the ability of the propensity scores to control for treatment selection bias.
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