CMAverse: A Suite of Functions for Reproducible Causal Mediation Analyses

Affiliations

¹ Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY.
² Swiss Data Science Center, Lausanne, Switzerland.
³ Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA.
⁴ Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Baoyi Shi et al. Epidemiology. 2021.

. 2021 Sep 1;32(5):e20-e22.

doi: 10.1097/EDE.0000000000001378.

¹ Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY.
² Swiss Data Science Center, Lausanne, Switzerland.
³ Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA.
⁴ Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

No abstract available

Conflict of interest statement

The authors report no conflicts of interest.

1. VanderWeele TJ. Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press; 2015.
1. Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18:137–150.
1. VanderWeele TJ, Vansteelandt S. Mediation analysis with multiple mediators. Epidemiol Methods. 2014;2:95–115.
1. Tchetgen Tchetgen EJ. Inverse odds ratio-weighted estimation for causal mediation analysis. Stat Med. 2013;32:4567–4580.
1. Vansteelandt S, Bekaert M, Lange T. Imputation strategies for the estimation of natural direct and indirect effects. Epidemiologic Methods. 2012;1:131–158.