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Table 2 Simulation Study Results: Relative bias (Bias), RMSE, and 95% coverage (Cov.) of the risk difference under various spatial variances (rows) and estimation methods (columns)

From: Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies

  Non-spatial Spatial
Unadjusted Adjusted Unadjusted Adjusted
Bias RMSE Cov. Bias RMSE Cov. Bias RMSE Cov. Bias RMSE Cov.
\(\sigma ^2_{u}=0\) 0.043 0.005 96 0.040 0.005 95 0.035 0.004 95 0.050 0.006 94
\(\sigma ^2_{u}=2\) 0.084 0.011 77 0.085 0.011 75 0.064 0.009 90 0.052 0.007 92
\(\sigma ^2_{u}=4\) 0.096 0.012 70 0.100 0.012 66 0.064 0.008 93 0.049 0.006 95
  1. \(\sigma ^2_{u}=0\) represents a non-spatial scenario that excludes county-level covariates \(V_{i}\) and \(U_{i}\). \(\sigma ^2_{u}=2\) and \(\sigma ^2_{u}=4\) represent spatial scenarios that include county-level covariates \(V_{i}\) and \(U_{i}\) where the spatial variance of \(U_{i}\) is either 2 or 4, respectively