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%GI: A SAS Macro for Measuring and Testing Global Imbalance of Covariates within Subgroups

Author

Listed:
  • Camillo, Furio
  • D'Attoma, Ida

Abstract

The global imbalance (GI) measure is a way for checking balance of baseline covariates that confound efforts to draw valid conclusions about treatment effects 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 classification methods (e.g., cluster analysis, propensity score subclassification, Rosenbaum and Rubin'84), 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 artificial examples.

Suggested Citation

  • Camillo, Furio & D'Attoma, Ida, 2012. "%GI: A SAS Macro for Measuring and Testing Global Imbalance of Covariates within Subgroups," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(c01).
    • Handle: RePEc:jss:jstsof:v:051:c01
    DOI: http://hdl.handle.net/10.18637/jss.v051.c01
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    References listed on IDEAS

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    1. Iacus, Stefano & King, Gary & Porro, Giuseppe, 2009. "cem: Software for Coarsened Exact Matching," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i09).
    2. Li, Qi & Maasoumi, Esfandiar & Racine, Jeffrey S., 2009. "A nonparametric test for equality of distributions with mixed categorical and continuous data," Journal of Econometrics, Elsevier, vol. 148(2), pages 186-200, February.
    3. Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2011. "Multivariate Matching Methods That Are Monotonic Imbalance Bounding," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 345-361.
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    Cited by:

    1. Stefano VERZILLO & Paolo BERTA & Matteo BOSSI, 2015. "%CEM: A SAS Macro to Perform Coarsened Exact Matching," Departmental Working Papers 2015-22, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

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