In our case, the propensity score that lies within the interval [0.37, 0.80] overlaps for both the treated and the control groups (see Figure 2 for Kernel matching method).
In each case, the propensity score was estimated using a logistic regression model to regress the treatment indicator variable (Z) on the baseline covariates (x1– x1).
In this case, the propensity score can be thought of as the conditional probability that a person responds given the set of covariates.
This change can be best visualized by considering the case of propensity score matching: including an additional variable in the propensity score used for matching would be expected to weaken at least slightly the tightness of the match on the other covariates.
Controls were matched to cases by a propensity score developed for data analysis.
The consistency of most findings from both full cohort with complete case and propensity score-matched cohort analytical approaches strongly suggested that use of metformin is comparatively beneficial to diabetic patients in terms of their clinical parameters and clinical outcomes.
The 474 IVF cases were then matched one-to-n with 474 non-IVF cases with the closest propensity score (nearest neighbor matching) [ 9, 10].
Among these high-risk groups, hospitalizations with mucormycosis were identified by the ICD-9-CM diagnosis code 117.7 (Zygomycosis [Phycomycosis or Mucormycosis]; cases) and matched to high-risk hospitalizations without a mucormycosis diagnosis (non-cases) based on a propensity score (PS).
Kaplan and Chen (2012) conducted three simulation studies as well as a small case study comparing frequentist propensity score analysis with the two-step Bayesian alternative focusing on the estimated treatment effect and variance estimates.
