Improving the Power to Detect Indirect Effects in Mediation Analysis

John Kidd () and Dan-Yu Lin ()
Additional contact information
John Kidd: University of North Carolina at Chapel Hill
Dan-Yu Lin: University of North Carolina at Chapel Hill

Statistics in Biosciences, 2024, vol. 16, issue 1, No 7, 129-141

Abstract: Abstract Causal mediation analysis seeks to determine whether an independent variable affects a response variable directly or whether it does so indirectly, by way of a mediator. The existing statistical tests to determine the existence of an indirect effect are overly conservative or have inflated type I error. In this article, we consider the principle of intersection–union tests and a method called the S-test. This method increases power but is not appropriate for statistical tests as small significance levels may cause the test to reject a null hypothesis, but larger significance levels will not reject the same hypothesis. We propose two new methods that provide increased power over existing methods while controlling type I error. We demonstrate through extensive simulation that the S-test and proposed methods control type I error and increase power over existing methods, and that while the proposed methods do not have the same problems, they provide similar power to the S-test. Finally, we provide an application to a large proteomic study.

Keywords: Intersection–union test; Sobel test; Product-normal distribution; Joint significance test; S-test; p value threshold (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-023-09386-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:16:y:2024:i:1:d:10.1007_s12561-023-09386-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-023-09386-6

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().