In EDS ansehen

Nonparametric estimation of path‐specific effects in the presence of nonignorable missing covariates.

Gespeichert in:
Bibliographische Detailangaben
Titel: Nonparametric estimation of path‐specific effects in the presence of nonignorable missing covariates.
Autoren: Shan, Jiawei1 (AUTHOR), Wang, Ting2 (AUTHOR), Li, Wei2 (AUTHOR) weilistat@ruc.edu.cn, Ai, Chunrong3 (AUTHOR)
Quelle: Scandinavian Journal of Statistics. Dec2025, Vol. 52 Issue 4, p1556-1593. 38p.
Schlagwörter: MEDIATION (Statistics), MISSING data (Statistics), NONPARAMETRIC estimation
Abstract: The path‐specific effect (PSE) is of primary interest in mediation analysis when multiple intermediate variables are in the pathway from treatment to outcome, as it can isolate the specific effect through each mediator, thus mitigating potential bias arising from other intermediate variables serving as mediator‐outcome confounders. However, estimation and inference of PSE become challenging in the presence of nonignorable missing covariates, a situation particularly common in studies involving sensitive individual information. This paper proposes a fully nonparametric methodology to address this challenge. We establish identification for PSE by expressing it as a function of observed data. By leveraging a shadow variable, we demonstrate that the associated nuisance functions can be uniquely determined through sequential optimization problems. Then, we propose a sieve‐based regression imputation approach for estimation. We establish the large‐sample theory for the proposed estimator and introduce an approach to make inferences for PSE. The proposed method is applied to the NHANES dataset to investigate the mediation roles of dyslipidemia and obesity in the pathway from Type 2 diabetes mellitus to cardiovascular disease. [ABSTRACT FROM AUTHOR]
Copyright of Scandinavian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Business Source Index
Beschreibung
Abstract:The path‐specific effect (PSE) is of primary interest in mediation analysis when multiple intermediate variables are in the pathway from treatment to outcome, as it can isolate the specific effect through each mediator, thus mitigating potential bias arising from other intermediate variables serving as mediator‐outcome confounders. However, estimation and inference of PSE become challenging in the presence of nonignorable missing covariates, a situation particularly common in studies involving sensitive individual information. This paper proposes a fully nonparametric methodology to address this challenge. We establish identification for PSE by expressing it as a function of observed data. By leveraging a shadow variable, we demonstrate that the associated nuisance functions can be uniquely determined through sequential optimization problems. Then, we propose a sieve‐based regression imputation approach for estimation. We establish the large‐sample theory for the proposed estimator and introduce an approach to make inferences for PSE. The proposed method is applied to the NHANES dataset to investigate the mediation roles of dyslipidemia and obesity in the pathway from Type 2 diabetes mellitus to cardiovascular disease. [ABSTRACT FROM AUTHOR]
ISSN:03036898
DOI:10.1111/sjos.70002