A comparison of two methods of estimating propensity scores after multiple imputation

Mitra, R. and Reiter, J.P. (2012) A comparison of two methods of estimating propensity scores after multiple imputation. Statistical Methods in Medical Research, 1-17. (doi:10.1177/0962280212445945).


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In many observational studies, analysts estimate treatment effects using propensity scores, e.g. by matching or sub-classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implement this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first, particularly when the missing values are predictive of treatment assignment.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1177/0962280212445945
ISSNs: 0962-2802 (print)
1477-0334 (electronic)
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
Faculty of Social and Human Sciences > Southampton Statistical Sciences Research Institute
ePrint ID: 180701
Accepted Date and Publication Date:
11 June 2012Made publicly available
Date Deposited: 12 Apr 2011 14:40
Last Modified: 31 Mar 2016 13:35
URI: http://eprints.soton.ac.uk/id/eprint/180701

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