Estimating the natural indirect effect and the mediation proportion via the product method

Abstract Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure...

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Autores principales:	Chao Cheng, Donna Spiegelman, Fan Li
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Estimating equations Mediation analysis Natural indirect effect Total effect Product method Asymptotically uncorrelated Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/8c5652d0656e4051851d9577b30e65c2
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spelling	oai:doaj.org-article:8c5652d0656e4051851d9577b30e65c22021-11-21T12:40:34ZEstimating the natural indirect effect and the mediation proportion via the product method10.1186/s12874-021-01425-41471-2288https://doaj.org/article/8c5652d0656e4051851d9577b30e65c22021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01425-4https://doaj.org/toc/1471-2288Abstract Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure and another model describing the exposure–mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. Methods With four common data types with a continuous/binary outcome and a continuous/binary mediator, we propose closed-form interval estimators for NIE and MP via the theory of multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Results Simulation studies suggest that the proposed interval estimator provides satisfactory coverage when the sample size ≥500 for the scenarios with a continuous outcome and sample size ≥20,000 and number of cases ≥500 for the scenarios with a binary outcome. In the binary outcome scenarios, the approximate estimators based on the rare outcome assumption worked well when outcome prevalence less than 5% but could lead to substantial bias when the outcome is common; in contrast, the exact estimators always perform well under all outcome prevalences considered. Conclusions Under samples sizes commonly encountered in epidemiology and public health research, the proposed interval estimator is valid for constructing confidence interval. For a binary outcome, the exact estimator without the rare outcome assumption is more robust and stable to estimate NIE and MP. An R package mediateP is developed to implement the methods for point and variance estimation discussed in this paper.Chao ChengDonna SpiegelmanFan LiBMCarticleEstimating equationsMediation analysisNatural indirect effectTotal effectProduct methodAsymptotically uncorrelatedMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Estimating equations Mediation analysis Natural indirect effect Total effect Product method Asymptotically uncorrelated Medicine (General) R5-920
spellingShingle	Estimating equations Mediation analysis Natural indirect effect Total effect Product method Asymptotically uncorrelated Medicine (General) R5-920 Chao Cheng Donna Spiegelman Fan Li Estimating the natural indirect effect and the mediation proportion via the product method
description	Abstract Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure and another model describing the exposure–mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. Methods With four common data types with a continuous/binary outcome and a continuous/binary mediator, we propose closed-form interval estimators for NIE and MP via the theory of multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Results Simulation studies suggest that the proposed interval estimator provides satisfactory coverage when the sample size ≥500 for the scenarios with a continuous outcome and sample size ≥20,000 and number of cases ≥500 for the scenarios with a binary outcome. In the binary outcome scenarios, the approximate estimators based on the rare outcome assumption worked well when outcome prevalence less than 5% but could lead to substantial bias when the outcome is common; in contrast, the exact estimators always perform well under all outcome prevalences considered. Conclusions Under samples sizes commonly encountered in epidemiology and public health research, the proposed interval estimator is valid for constructing confidence interval. For a binary outcome, the exact estimator without the rare outcome assumption is more robust and stable to estimate NIE and MP. An R package mediateP is developed to implement the methods for point and variance estimation discussed in this paper.
format	article
author	Chao Cheng Donna Spiegelman Fan Li
author_facet	Chao Cheng Donna Spiegelman Fan Li
author_sort	Chao Cheng
title	Estimating the natural indirect effect and the mediation proportion via the product method
title_short	Estimating the natural indirect effect and the mediation proportion via the product method
title_full	Estimating the natural indirect effect and the mediation proportion via the product method
title_fullStr	Estimating the natural indirect effect and the mediation proportion via the product method
title_full_unstemmed	Estimating the natural indirect effect and the mediation proportion via the product method
title_sort	estimating the natural indirect effect and the mediation proportion via the product method
publisher	BMC
publishDate	2021
url	https://doaj.org/article/8c5652d0656e4051851d9577b30e65c2
work_keys_str_mv	AT chaocheng estimatingthenaturalindirecteffectandthemediationproportionviatheproductmethod AT donnaspiegelman estimatingthenaturalindirecteffectandthemediationproportionviatheproductmethod AT fanli estimatingthenaturalindirecteffectandthemediationproportionviatheproductmethod
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Estimating the natural indirect effect and the mediation proportion via the product method

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