Analyzing propensity matched zero-inflated count outcomes in observational studies

DOI10.1080/02664763.2013.834296OpenAlexW1990536517WikidataQ42200051 ScholiaQ42200051MaRDI QIDQ5128568

Christos Lazaridis, Shuang Ji, Francis G. Spinale, Stacia M. DeSantis

Publication date: 28 October 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3843491

zbMATH Keywords

count datarandom effectsPoissonzero inflationpropensity matching

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• SAS

Cites Work

• Longitudinal data analysis using generalized linear models
• Linear and generalized linear mixed models and their applications.
• Covariate balance in simple, stratified and clustered comparative studies
• Confounding and collapsibility in causal inference
• Covariance adjustment in radomized experiments and observational studies
• Matched Sampling for Causal Effects
• Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros
• The central role of the propensity score in observational studies for causal effects
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
• Directional penalties for optimal matching in observational studies
• Random effect models for repeated measures of zero-inflated count data
• Observational studies.

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