This article also discusses weighting and imputation, alternative analyses used to assess the adequacy of the propensity methods, and the challenges involved in media campaign evaluations.
Findings remained consistent in sensitivity analyses by using propensity methods to account for treatment selection.
When we applied propensity methods in the primary analyses, the differences seen were also significant.
Studies have shown that propensity methods can remove up to 90%% of the bias resulting from observable confounders [ 28, 29].
We used both propensity methods and an instrumental variable (IV) approach to account for imbalances in baseline patient risk.
Although we used propensity scoring to assess for significant associations with post-ICD treatment, propensity methods can only account for variables that are measured.
To reduce potential confounding by indication bias we used 2 different propensity methods to balance the covariates among patients undergoing the 2 revascularization strategies.
In this paper, we present an example of using propensity methods to balance covariates when assessing the rate of adverse events in drug registry data.
A lack of common support, or a complete separation of propensity scores between the exposure groups indicates severe differences between the two exposure groups and the possibility that confounding cannot be reduced using propensity methods [ 22].
