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medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models

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  • Steen, Johan
  • Loeys, Tom
  • Moerkerke, Beatrijs
  • Vansteelandt, Stijn

Abstract

Mediation analysis is routinely adopted by researchers from a wide range of applied disciplines as a statistical tool to disentangle the causal pathways by which an exposure or treatment affects an outcome. The counterfactual framework provides a language for clearly defining path-specific effects of interest and has fostered a principled extension of mediation analysis beyond the context of linear models. This paper describes medflex, an R package that implements some recent developments in mediation analysis embedded within the counterfactual framework. The medflex package offers a set of ready-made functions for fitting natural effect models, a novel class of causal models which directly parameterize the path-specific effects of interest, thereby adding flexibility to existing software packages for mediation analysis, in particular with respect to hypothesis testing and parsimony. In this paper, we give a comprehensive overview of the functionalities of the medflex package.

Suggested Citation

  • Steen, Johan & Loeys, Tom & Moerkerke, Beatrijs & Vansteelandt, Stijn, 2017. "medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i11).
    • Handle: RePEc:jss:jstsof:v:076:i11
    DOI: http://hdl.handle.net/10.18637/jss.v076.i11
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    2. Hargrove, Emily M. & Stults, Brian J. & Hay, Carter & Meldrum, Ryan C., 2023. "Sleep duration as a mediator of the effects of risk factors for substance use," Journal of Criminal Justice, Elsevier, vol. 88(C).
    3. Anita Lindmark, 2022. "Sensitivity analysis for unobserved confounding in causal mediation analysis allowing for effect modification, censoring and truncation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 785-814, October.
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    5. Caubet, Miguel & Samoilenko, Mariia & Drouin, Simon & Sinnett, Daniel & Krajinovic, Maja & Laverdière, Caroline & Marcil, Valérie & Lefebvre, Geneviève, 2023. "Bayesian joint modeling for causal mediation analysis with a binary outcome and a binary mediator: Exploring the role of obesity in the association between cranial radiation therapy for childhood acut," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    6. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    7. N. Dardenne & B. Pétré & E. Husson & M. Guillaume & A. F. Donneau, 2020. "Assessing Quality of Life in an Obesity Observational Study: a Structural Equation Modeling Approach," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 15(4), pages 1117-1133, September.
    8. Nevo Daniel & Liao Xiaomei & Spiegelman Donna, 2017. "Estimation and Inference for the Mediation Proportion," The International Journal of Biostatistics, De Gruyter, vol. 13(2), pages 1-18, November.
    9. Meldrum, Ryan C. & Stults, Brian J. & Hay, Carter & Kernsmith, Poco D. & Smith-Darden, Joanne P., 2022. "Adverse childhood experiences, developmental differences in impulse control and sensation seeking, and delinquency: A prospective multi-cohort study," Journal of Criminal Justice, Elsevier, vol. 82(C).
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