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Since balancing the two groups to each other changes the average sample characteristics compared to the population, we in effect measure the sample average treatment effect on the treated (SATT) when applying CEM.
On the other hand, cDiD can, by balancing the two groups, mitigate the bias occurring when, for instance, the two groups have different age compositions, something which can give rise to diverging income progressions (steeper for younger people).
The selection of the covariates for balancing the two groups was made according to the results of previous studies from our cohort investigating prognostic factors in HIV infected patients with tuberculosis [ 7, 20].
We will ensure that the groups are more homogeneous moving forward by periodically balancing the two groups through ensuring that the comparison group is age-matched and sex-matched to the survivors group.
In addition to serving as a final data quality check, these analyses are used to characterize the study population, assess the success of randomization in balancing the two groups, and determine the impact of any dropouts on that balance with respect to potentially important prognostic factors (e.g., age, education level of Veteran, MMSE score).
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Propensity score weighting was used to balance the two groups.
One way of applying the matching approach is to weight the most crucial variables in the estimation in order to balance the two groups so that they look very similar along observable dimensions.
Randomisation will balance the two groups by site of pain, age, gender and duration of symptoms.
The propensity score adjustment was used in order to balance the two groups in relation to these differences.
The use of propensity score for adjustments enabled us to balance the two groups regarding numerous important covariates.
Due to the small sample size and random chance, the randomization did not completely balance the two groups for all potential confounders.
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