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. 2014 Jan 1;41(1):10.1080/02664763.2013.834296.
doi: 10.1080/02664763.2013.834296.

Analyzing Propensity Matched Zero-Inflated Count Outcomes in Observational Studies

Stacia M Desantis  1 , Christos Lazaridis, Shuang Ji, Francis G Spinale
Affiliations

Analyzing Propensity Matched Zero-Inflated Count Outcomes in Observational Studies

Stacia M Desantis et al. J Appl Stat. .

Abstract

Determining the effectiveness of different treatments from observational data, which are characterized by imbalance between groups due to lack of randomization, is challenging. Propensity matching is often used to rectify imbalances among prognostic variables. However, there are no guidelines on how appropriately to analyze group matched data when the outcome is a zero inflated count. In addition, there is debate over whether to account for correlation of responses induced by matching, and/or whether to adjust for variables used in generating the propensity score in the final analysis. The aim of this research is to compare covariate unadjusted and adjusted zero-inflated Poisson models that do and do not account for the correlation. A simulation study is conducted, demonstrating that it is necessary to adjust for potential residual confounding, but that accounting for correlation is less important. The methods are applied to a biomedical research data set.

Keywords: Poisson; count data; propensity matching; random effects; zero inflation.

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Figures

Figure 1
Figure 1
Quality of the matched sample as indicated by the caliper within which matching is possible.
Figure 2
Figure 2
Q–Q plots assessing the quality of matching for the three continuous covariates: cross-clamp time, perfusion time and Euro score, respectively.
See this image and copyright information in PMC

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