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The number of appropriate strata depends on the number necessary to gain a balanced propensity score.
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Covariates were adequately balanced after propensity score matching, as evidenced by a standardized difference of each covariate to values below 0.1 [ 18].
All baseline demographic and clinical characteristics, health care utilizations, and health care costs were balanced after propensity score matching (Table 1).
To minimize the effect of confounding and obtain an unbiased estimate of the treatment effect, differences in baseline characteristics of the two groups were balanced using propensity score methods to estimate the average treatment effect.
41 42 In this study, we observed significant differences in the rates of use of chemotherapy by institution, although they were balanced after propensity weighting and therefore did not influence the observed associations between receipt of chemotherapy and patients' end of life medical care and place of death.
The uniformity in the measured risk factor distribution indicates that the distribution of unmeasured variables is balanced, although propensity scores cannot remove hidden biases except to the extent that the unmeasured prognostic variables are correlated with the measured covariates used to compute the score.
We introduce covariate balancing propensity score (CBPS) methodology, which models treatment assignment while optimizing the covariate balance.
To address this problem, we generalize the covariate balancing propensity score (CBPS) methodology of Imai and Ratkovic to longitudinal analysis settings.
Prognostic factors were well balanced across the propensity strata.
Baseline characteristics were balanced in the propensity matched populations.
The differences in baseline characteristics between PD and HD patients were balanced in the propensity matching procedure (Table 1).
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