Exploratory Mediation Analysis of Dichotomous Outcomes via Regularization

Abstract

Exploratory mediation analysis via regularization, or XMed, is a recently developed technique that allows one to identify potential mediators of a process of interest. However, as currently implemented, it can only be applied to continuous outcomes. We extend this method to allow application to dichotomous outcomes, including both mediators and dependent variables. Simulation results show that XMed can achieve the same sensitivity as more conventional methods for mediation analysis such as the Sobel test, percentile bootstrap, and bias-corrected bootstrap, but in general requires only half the sample size to do so. We demonstrate the implementation of this approach using an illustrative example examining the relationship between youth behavioral/emotional problems and alcohol use.

Keywords:

• Mediation
• exploratory mediation analysis
• regularization
• lasso
• dichotomous

Article Information

Conflict of Interest Disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical Principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was not supported.

Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgments: The authors would like to thank Demi Culianos for her assistance in putting together this manuscript. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors' institutions is not intended and should not be inferred.

Notes

1 A detailed example of how to apply the function using a publicly available dataset is provided in the manual for the regsem package.

2 As the bootstrap procedures in lavaan do not refer to the bootstrapping methods commonly used in mediation analysis (i.e., the ones described in this manuscript) we rely on lavaan only to calculate parameter estimates. We wrote our own R code to perform the bootstrapping based on the procedures outlined in the text, which is available from the first author upon request.