bal.tab() with Multi-Category TreatmentsWhen using bal.tab() with multi-category treatments, the output will be different from the case with binary or continuous treatments, and there are some options that are common across all bal.tab() methods. This page outlines the outputs and options in this case.
There are two main components of the output of bal.tab() with multi-category treatments: the two-group treatment comparisons and the balance summary. The two-group treatment comparisons are standard binary treatment comparison either for pairs of groups (e.g., for treatments A, B, and C, "A vs. B", "A vs. C", and "B vs. C") or each group against all the groups (i.e., the entire sample).
The balance summary is, for each variable, the greatest imbalance across all two-group comparisons. So, for variable X1, if "A vs. B" had a standardized mean difference of 0.52, "A vs. C" had a standardized mean difference of .17, and "B vs. C" had a standardized mean difference of .35, the balance summary would have 0.52 for the value of the standardized mean difference for X1. The same goes for other variables and other measures of balance. If the greatest observed imbalance is tolerable, then all other imbalances for that variable will be tolerable too, so focusing on reducing the greatest imbalance is sufficient for reducing imbalance overall. (Note that when s.d.denom = "pooled", i.e., when the estimand is the ATE, the pooled standard deviation in the denominator will be the average of the standard deviations across all treatment groups, not just those used in the pairwise comparison.) The balance summary will not be computed if multiply imputed data are used.
There are four arguments for each bal.tab() method that can handle multi-category treatments: pairwise, focal, which.treat, and multi.summary.
pairwiseWhether to compute the two-group comparisons pairwise or not. If TRUE, bal.tab() will compute comparisons for each pair of treatments. This can be valuable if treatments are to be compared with one another (which is often the case). If FALSE, bal.tab() will compute balance for each treatment group against the full unadjusted sample when focal is NULL and for each non-focal group against the focal group otherwise.
focalWhen one group is to be compared to multiple control groups in an ATT analysis, the group considered "treated" is the focal group. By specifying the name or index of the treatment condition considered focal, bal.tab() will only compute and display pairwise balance for treatment comparisons that include the focal group when pairwise = FALSE.
which.treatThis is a display option that does not affect computation. When displaying the bal.tab output, which treatments should be displayed? If a vector of length 1 is entered, all comparisons involving that treatment group will be displayed. If a vector of length 2 or more is entered, all comparisons involving treatments that both appear in the input will be displayed. For example, inputting "A" will display "A vs. B" and "A vs. C", while entering c("A", "B") will only display "A vs. B". .none indicates no treatment comparisons will be displayed, and .all indicates all treatment comparisons will be displayed. .none is the default.
multi.summaryIf TRUE, the balance summary across all comparisons will be computed and displayed. This includes one row for each covariate with maximum balance statistic across all pairwise comparisons. Note that, if variance ratios or KS statistics are requested in addition to mean differences, the displayed values may not come from the same pairwise comparisons; that is, the greatest standardized mean difference and the greatest variance ratio may not come from the same comparison. The default is TRUE, and if which.treat is .none, it will automatically be set to TRUE.
The output is a bal.tab.multi object, which inherits from bal.tab. It has the following elements:
Pair.Balance:For each pair of treatment groups, a regular bal.tab object containing a balance table, a sample size summary, and other balance assessment tools, depending on which options are specified. If pairwise is FALSE, the comparisons will be between each group and the groups combined (labeled "All") when focal is NULL and between each non-focal group and the focal group otherwise.
Balance.Across.Pairs: The balance summary across two-group comparisons. This will include the greatest (i.e., maximum) absolute balance statistics(s) for each covariate across all comparisons computed. Thresholds can be requested for each balance measure as with binary treatments.
Observations: A table of sample sizes or effective sample sizes for each treatment group before and after adjustment.
As with other methods, multiple weights can be specified, and values for all weights will appear in all tables.
bal.tab()
bal.tab.data.frame()
print.bal.tab()
vignette("segmented-data") for examples