Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies

Abstract

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.

Keywords:

• Mediation
• causal modeling
• longitudinal data analysis
• measurement models
• factor scores

Acknowledgments

The authors would like to thank Fien Gistelinck, Ines Devlieger, Justine Loncke, Mariska Barendse, Yves Rosseel, the Editor, Associate Editor, and three reviewers for their comments on prior versions of this manuscript. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government – department EWI. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institutions or Research Foundation – Flanders (FWO) is not intended and should not be inferred.

Article information

Conflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. Wen Wei Loh was supported by Grant G019317N from the Research Foundation – Flanders (FWO). Louise Poppe was supported by Grant 11Z4716N from the Research Foundation – Flanders (FWO). The other authors reported no 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 supported by Grants G019317N and 11Z4716N from the Research Foundation – Flanders (FWO).

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.