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Statistics in Medicine
Article . 2023 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
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zbMATH Open
Article . 2023
Data sources: zbMATH Open
https://dx.doi.org/10.48550/ar...
Article . 2023
License: CC BY
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A novel causal mediation analysis approach for zero‐inflated mediators

A novel causal mediation analysis approach for zero-inflated mediators
Authors: Meilin Jiang; Seonjoo Lee; A. James O'Malley; Yaakov Stern; Zhigang Li;

A novel causal mediation analysis approach for zero‐inflated mediators

Abstract

Mediation analyses play important roles in making causal inference in biomedical research to examine causal pathways that may be mediated by one or more intermediate variables (ie, mediators). Although mediation frameworks have been well established such as counterfactual‐outcomes (ie, potential‐outcomes) models and traditional linear mediation models, little effort has been devoted to dealing with mediators with zero‐inflated structures due to challenges associated with excessive zeros. We develop a novel mediation modeling approach to address zero‐inflated mediators containing true zeros and false zeros. The new approach can decompose the total mediation effect into two components induced by zero‐inflated structures: the first component is attributable to the change in the mediator on its numerical scale which is a sum of two causal pathways and the second component is attributable only to its binary change from zero to a non‐zero status. An extensive simulation study is conducted to assess the performance and it shows that the proposed approach outperforms existing standard causal mediation analysis approaches. We also showcase the application of the proposed approach to a real study in comparison with a standard causal mediation analysis approach.

Keywords

zero-inflated negative binomial, FOS: Computer and information sciences, Models, Statistical, Mediation Analysis, zero-inflated log-normal, Mathematics - Statistics Theory, Statistics Theory (math.ST), Statistics - Applications, Applications of statistics to biology and medical sciences; meta analysis, Causality, Linear Models, FOS: Mathematics, Humans, mediation, zero-inflated mediator, Computer Simulation, Applications (stat.AP), causal inference

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
Green