This paper presents the development of the data driven approach first introduced in Camillo and D’Attoma (2010) and D’Attoma (2009), which enabled one to obtain a global measure of comparability between treatment groups within a non-experimental framework. This paper points to better formalize the global measure of imbalance reported in Camillo and D’Attoma (2010) and D’Attoma (2009) and to introduce a multivariate imbalance test. We consider the global measure of imbalance and the multivariate imbalance test as tools for investigating the dependence relationship between categorical covariates and the assignment-to-treatment indicator variable within a more complex strategy whose final aim is to find balanced groups. We will show in simulated data how the strategy works in practice.

A Multivariate Strategy to Measure and Test Global Imbalance in Observational Studies

D'ATTOMA, IDA;CAMILLO, FURIO
2011

Abstract

This paper presents the development of the data driven approach first introduced in Camillo and D’Attoma (2010) and D’Attoma (2009), which enabled one to obtain a global measure of comparability between treatment groups within a non-experimental framework. This paper points to better formalize the global measure of imbalance reported in Camillo and D’Attoma (2010) and D’Attoma (2009) and to introduce a multivariate imbalance test. We consider the global measure of imbalance and the multivariate imbalance test as tools for investigating the dependence relationship between categorical covariates and the assignment-to-treatment indicator variable within a more complex strategy whose final aim is to find balanced groups. We will show in simulated data how the strategy works in practice.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/106385
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