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

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.

[1]  Gary King,et al.  Misunderstandings between experimentalists and observationalists about causal inference , 2008 .

[2]  D. Rubin.  Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation , 2001, Health Services and Outcomes Research Methodology.

[3]  Gary King,et al.  Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.

[4]  Li Qi.  A NONPARAMETRIC TEST FOR EQUALITY OF DISTRIBUTIONS WITH MIXED CATEGORICAL AND CONTINUOUS DATA , 2008 .

[5]  O. Baser,et al.  Too much ado about propensity score models? Comparing methods of propensity score matching. , 2006, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[6]  Rajeev Dehejia,et al.  Propensity Score-Matching Methods for Nonexperimental Causal Studies , 2002, Review of Economics and Statistics.

[7]  Josep Daunis-i-Estadella,et al.  Distribution of the inter and intra inertia in conditional MCA , 2005, Comput. Stat..

[8]  G. King,et al.  Cem: Coarsened Exact Matching in Stata , 2009 .

[9]  M. Greenacre.  Theory of Correspondence Analysis , 2007 .

[10]  Brigitte Escofier.  Analyse des correspondances multiples conditionnelle , 1990, Monde des Util. Anal. Données.

[11]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[12]  D. Rubin.  Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies , 1973 .

[13]  Jake Bowers,et al.  Covariate balance in simple stratified and clustered comparative studies , 2008, 0808.3857.

[14]  Ida D'Attoma,et al.  A new data mining approach to estimate causal effects of policy interventions , 2010, Expert Syst. Appl..

[15]  Jeffrey S. Racine,et al.  Nonparametric Econometrics: The np Package , 2008 .

[16]  Entropy-Based Inference using R and the np Package: A Primer , 2010 .

[17]  P. Duncombe,et al.  Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices , 1985 .

[18]  Stefano M. Iacus,et al.  cem: Software for Coarsened Exact Matching , 2009, Journal of Statistical Software.

[19]  Laura R. Peck,et al.  A Promising New Approach to Eliminating Selection Bias , 2009, Canadian Journal of Program Evaluation.

[20]  A. Heshmati,et al.  A Microeconometric Evaluation of Rehabilitation of Long-term Sickness in Sweden , 2004 .

[21]  Ida D'Attoma,et al.  A multivariate strategy to measure and test global imbalance in observational studies , 2011, Expert Syst. Appl..

[22]  Laura R. Peck,et al.  Using Cluster Analysis in Program Evaluation , 2005, Evaluation review.

[23]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[24]  Xiao-Hua Zhou,et al.  Generalized propensity score for estimating the average treatment effect of multiple treatments , 2012, Statistics in medicine.

[25]  G. King,et al.  Multivariate Matching Methods That Are Monotonic Imbalance Bounding , 2011 .