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Data-balance correction was checked and confirmed in each quintile on all covariates used in each propensity score model.
In contrast, only 4 of 16 (25%) and 2 of 8 (25%) died of HCC recurrence in the LT group in each propensity score analysis.
In the HR group, 16 of 19 patients (84%) and 9 of 11 patients (82%) died of HCC recurrence in each propensity score analysis.
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Variables within each propensity score quintile are shown in table 2. Propensity scoring by quintile failed to balance a number of covariates across all quintiles.
To explore this possibility, we calculated the rate ratios in each quintile of propensity score separately (Table 3).
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).
The balance of the covariates in each of the propensity score strata in the present study was examined by chi-square tests of association of each covariate with each of the categorised social network variables [ 23].
The issues now are managerial and regulatory – how to save markets from their own built-in propensity to blow up.
We considered the standardized difference of each covariate in the propensity score model.
One hundred ten patients (55 patients in each group) were included in the propensity score (Table 3).
Whereas standardized differences and differences rely on the cross-sample difference of each variable included in the propensity score matching model, Hotelling's T-square test considers whether these differences can be taken as jointly insignificant.
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CEO of Professional Science Editing for Scientists @ prosciediting.com