کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
386002 | 660876 | 2011 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A multivariate strategy to measure and test global imbalance in observational studies A multivariate strategy to measure and test global imbalance in observational studies](/preview/png/386002.png)
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.
Research highlights
► We introduce a strategy for making causal inference from observational data without model dependence.
► The Global Measure of Imbalance represents a measure of comparability between-groups.
► The Multivariate Imbalance Coefficient expresses the importance of the measured imbalance relative to the total inertia.
► The multivariate test allows to determine if the measured imbalance is significant.
Journal: Expert Systems with Applications - Volume 38, Issue 4, April 2011, Pages 3451–3460