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Activity Number: 629
Type: Invited
Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #310831 View Presentation
Title: A Weighting Analogue to Pair Matching in Propensity Score Analysis
Author(s): Liang Li*+ and Tom Greene
Companies: MD Anderson Cancer Center and University of Utah
Keywords: Causal inference ; Double robust ; Inverse probability weighting ; Matching weight ; Mirror histogram ; Propensity Score
Abstract:

Pair matching with propensity scores is a popular method in medical research to study the effect of medical treatments. There are some controversies in the way this method is currently implemented in medical literature, including, most notably, balance checking and variance estimation. We discuss the issues and propose a weighting analogue to pair matching, called the matching weight, which avoids some of the methodological difficulties with pair matching. Compared with pair matching, the matching weight method offers more efficient estimation, more accurate variance calculation, better balance, and simpler asymptotic analysis. A statistical test for the misspecification of the propensity score model is proposed for balance checking purposes. An augmented version of the matching weight estimator is developed that has the double robust property, i.e., the estimator is consistent if either the outcome model or the propensity score model is correct. The proposed method is studied in simulations and illustrated through some real data examples.


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