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Analyzing propensity matched zero-inflated count outcomes in observational studies

Author

Listed:
  • Stacia M. DeSantis
  • Christos Lazaridis
  • Shuang Ji
  • Francis G. Spinale

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.

Suggested Citation

  • Stacia M. DeSantis & Christos Lazaridis & Shuang Ji & Francis G. Spinale, 2014. "Analyzing propensity matched zero-inflated count outcomes in observational studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 127-141, January.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:127-141
    DOI: 10.1080/02664763.2013.834296
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    References listed on IDEAS

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    1. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
    2. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
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