An optimal cluster-based approach for Subgroup Analysis using Information Complexity Criterion.

The global imbalance (GI) measure is a way for checking balance of baseline covariates that confound eorts to draw valid conclusions about treatment eects on outcomes of interest. In addition, GI is tested by means of a multivariate test. The GI measure and its test overcome some limitations of the common way for assessing the presence of imbalance in observed covariates that were discussed in D'Attoma and Camillo (2011). A user written SAS macro called %GI, to simultaneously measure and test global imbalance of baseline covariates is described. Furthermore, %GI also assesses global imbalance by subgroups obtained through several matching or classication methods (e.g., cluster analysis, propensity score subclassication, Rosenbaum and Rubin 1984), no matter how many groups are examined. %GI works with mixed categorical, ordinal and continuous covariates. Continuous baseline covariates need to be split into categories. It also works in the multi-treatment case. The use of the %GI macro will be illustrated using two articial examples