Complete effect decomposition for an arbitrary number of multiple ordered mediators with time-varying confounders: A method for generalized causal multi-mediation analysis

Tai, An‐Shun; Lin, Sheng‐Hsuan

doi:10.1177/09622802221130580

Cited by 2 publications

(1 citation statement)

References 61 publications

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“…Note that linking the regression coefficients to causal estimands often requires rigorous assumptions, such as no time-varying confounding (Bind et al 2016) or sequential no unmeasured confounding (VanderWeele and Tchetgen Tchetgen 2017). Furthermore, for high-dimensional mediators, the identifiability and decomposition of path-specific causal effects remains a challenging topic (Tai and Lin 2023), which could be our future research direction.…”

Section: Discussionmentioning

confidence: 99%

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

Wei,

Xue,

et al. 2024

Preprint

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DNA methylation (DNAm) has been shown to mediate causal effects from traumatic experiences to post-traumatic stress disorder (PTSD). However, the scientific question about whether the mediation effect changes over time remains unclear. In this paper, we develop time-varying structural equation models to identify cytosine-phosphate-guanine (CpG) sites where DNAm mediates the effect of trauma exposure on PTSD, and to capture dynamic changes in mediation effects. The proposed methodology is motivated by the Detroit Neighborhood Health Study (DNHS) with high-dimensional and longitudinal DNAm measurements. To handle the non-monotone missing DNAm in the dataset, we propose a novel Longitudinal Multiple Imputation (LMI) method utilizing dependency among repeated measurements, and employ the generalized method of moments to integrate the multiple imputations. Simulations confirm that the proposed method outperforms existing approaches in various longitudinal settings. In DNHS data analysis, our method identifies several CpG sites where DNAm exhibits dynamic mediation effects. Some of the corresponding genes have been shown to be associated with PTSD in the existing literature, and our findings on their time-varying effects could deepen the understanding of the mediation role of DNAm on the causal path from trauma exposure to PTSD risk.

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Section: Discussionmentioning

confidence: 99%

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

Wei,

Xue,

et al. 2024

Preprint

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Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders

Díaz

Williams

Rudolph

2023

Journal of Causal Inference

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Understanding the mechanisms of action of interventions is a major general goal of scientific inquiry. The collection of statistical methods that use data to achieve this goal is referred to as mediation analysis. Natural direct and indirect effects provide a definition of mediation that matches scientific intuition, but they are not identified in the presence of time-varying confounding. Interventional effects have been proposed as a solution to this problem, but existing estimation methods are limited to assuming simple (e.g., linear) and unrealistic relations between the mediators, treatments, and confounders. We present an identification result for interventional effects in a general longitudinal data structure that allows flexibility in the specification of treatment-outcome, treatment-mediator, and mediator-outcome relationships. Identification is achieved under the standard no-unmeasured-confounders and positivity assumptions. In this article, we study semi-parametric efficiency theory for the functional identifying the mediation parameter, including the non-parametric efficiency bound, and was used to propose non-parametrically efficient estimators. Implementation of our estimators only relies on the availability of regression algorithms, and the estimators in a general framework that allows the analyst to use arbitrary regression machinery were developed. The estimators are doubly robust, n \sqrt{n} -consistent, asymptotically Gaussian, under slow convergence rates for the regression algorithms used. This allows the use of flexible machine learning for regression while permitting uncertainty quantification through confidence intervals and p p -values. A free and open-source R package implementing the methods is available on GitHub. The proposed estimator to a motivating example from a trial of two medications for opioid-use disorder was applied, where we estimate the extent to which differences between the two treatments on risk of opioid use are mediated by craving symptoms.

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Complete effect decomposition for an arbitrary number of multiple ordered mediators with time-varying confounders: A method for generalized causal multi-mediation analysis

Cited by 2 publications

References 61 publications

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders

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