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Difference between revisions of "Integrating SAS® and R to Perform Optimal Propensity Score Matching"

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== Online Materials ==
 
== Online Materials ==
 
View the pdf [https://www.researchgate.net/publication/301650077_Integrating_SASR_and_R_to_Perform_Optimal_Propensity_Score_Matching here].
 
View the pdf [https://www.researchgate.net/publication/301650077_Integrating_SASR_and_R_to_Perform_Optimal_Propensity_Score_Matching here].
 
 
 
  
 
==Contact Info==
 
==Contact Info==

Revision as of 16:34, 6 May 2016

Abstract

In studies where randomization is not possible, imbalance in baseline covariates (confounding by indication) is a fundamental concern. Propensity score matching (PSM) is a popular method to minimize this potential bias, matching individuals who received treatment to those who did not to reduce the imbalance in pre-treatment covariate distributions. PSM methods continue to advance as computing resources expand. Optimal matching, which selects the set of matches that minimizes the average difference in propensity scores between mates, has been shown to outperform less computationally intensive methods. However, many find the implementation daunting. SAS/IML® software allows the integration of optimal matching routines that execute in R, e.g. the R nbpMatching package. This paper walks through performing optimal PSM in SAS® through implementing R functions. It covers the propensity score creation in SAS, the matching procedure, and the post-matching assessment of covariate balance using SAS/STAT® 13.2 and SAS/IML procedures.

Online Materials

View the pdf here.

Contact Info

Please check out my user page. You can also email me.