Abstract
Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction (MI) effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.
1 Introduction
Mediation analysis has become the technique of choice to identify and explain the mechanism that underlies an observed relationship between an exposure or treatment variable and an outcome variable via the inclusion of intermediate variables, known as mediators. Decompositions of the total effect (TE) of the exposure into effects characterizing mediation pathways and interactions help researchers understand the effects through different mechanisms and have gained much attention in the literature and application in the last decade [1,2, 3,4,5, 6,7,8, 9,10]. In our motivating example, we are interested in the effects of drinking alcohol on systolic blood pressure (SBP) via the mediators, body mass index (BMI), and gamma-glutamyl transferase (GGT), and their interaction effects. Besides, the mediator BMI is previously reported to affect GGT and not vice versa, and hence the two mediators are causally sequential [3]. Current developments in this area for scenarios considering two mediators have primarily focused on either decomposition without interaction components, or decomposition allowing interactions but assuming no causally sequential order between the mediators [3,4,9]. Daniel et al. [3] and Steen et al. [4] discussed the decompositions in a general framework with causally sequential mediators; however, their decompositions do not include interaction components. Bellavia and Valeri [9] proposed a decomposition with components describing interactions, but they assumed these mediators are causally non-sequential. Taguri et al. [10] also considered scenarios with multiple mediators that are causally non-ordered, in which they developed a novel component termed “mediated interaction” (MI).
In this work, we develop decomposition methods for the scenarios when the two mediators are causally sequential and the interaction effects among the mediators and exposure possibly exist. Our approach also applies to a non-sequential two-mediator scenario. We present a unified approach for decomposing the TE into the components that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Our decomposition methods are motivated by vanderWeele’s four-way decomposition [7] of the TE with one mediator, where the interaction effects include a reference interaction effect for interaction only and an MI effect for both mediation and interaction. VanderWeele [7] emphasized that these additive interaction terms are often considered of the greatest public health importance [11,12]. We also propose a new concept called natural MI effect for describing the two-way and three-way interactions in two-mediator scenarios that extend the MI from VanderWeele’s work [7]. Since the causal structures are more complex with two mediators, the decompositions have multiple terms for mediation only, interaction only, and both mediation and interaction. Identifiability issues appear in the presence of time-varying confounders, which will be naturally introduced by the mediators in a sequential structure [13,14]. We lay out the identification assumptions and provide identifiable counterfactual formulas in our proposed decomposition [15].
When the two mediators are casually non-sequential, our decomposition uses a different approach from what was proposed by Bellavia and Valeri [9]. For example, their population-averaged MI effect between
The rest of the article is organized as follows: Section 2 reviews VanderWeele’s four-way decomposition; Section 3 presents decompositions of TE for two-mediator scenarios; Section 4 relates the components of our proposed decompositions to the traditional definitions; Section 5 lays out identification assumptions and gives the empirical and regression-based formulas for computing each component in the decomposition with two causally sequential mediators; Section 6 presents a simulation study and real data analysis; and Section 7 concludes the article with discussions.
2 Decomposition of the TE in a single-mediator scenario
2.1 Counterfactual definitions
Consider a single-mediator scenario in Figure 1. Counterfactual formulas give the potential value of outcome
2.2 Two-way decomposition
The TE of the exposure
The second equality of TE follows by the composition axiom [8,15] and the third equality of TE follows by subtracting and adding the same counterfactual formula
2.3 Four-way decomposition with interactions
VanderWeele [7] proposed a four-way decomposition in a single-mediator scenario where the exposure interacts with the mediator. The TE of the exposure on the outcome is decomposed into components due to mediation only, interaction only, both mediation and interaction, and neither mediation nor interaction. These four components are termed as pure indirect effect (PIE), reference interaction effect (
The reference and MI effects can also be expressed in the form of the counterfactual formulas in our view:
CDE measures the effect of
When
where 1 is the treatment level and 0 is the reference level [7].
Both
Based on the counterfactual formula form of MI
Definition 1
We define the natural MI effect of
where
3 Decomposition of the TE in two-mediator scenarios
When two mediators are considered, two-way interaction of the two mediators and three-way interaction of the exposure and the two mediators are likely to exist [7,8,9]. There may also be a causal sequence between the two mediators, i.e., there is a direct causal link between the two mediators. There is limited research on how to define interactions when the two mediators are causally sequential. We aim to develop interpretable interaction concepts and decomposition approaches for two-mediator scenarios.
3.1 Mediators causally non-sequential
We first consider the scenario when the two mediators are causally non-sequential, i.e., there is no direct causal link between the two mediators, which is shown in Figure 3. Below, we define two-way natural MI effects of
Definition 2
Natural MI effects in a causally non-sequential two-mediator scenario are defined as follows:
The above three-way interaction measures the change in the two-way interaction between
In Supplementary material S1, we show that the TE can be decomposed into ten components at the individual level:
where
Similar to the four-way decomposition, CDE denotes controlled direct effect due to neither mediation nor interaction,
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Definition | Interpretation |
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Due to neither mediation nor interaction |
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Due to the interaction between
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Due to the interaction between
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Due to the interaction between
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through both
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Due to the mediation through both
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Due to the mediation through
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Due to the mediation through
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Bellavia and Valeri [9] proposed a ten-way decomposition for the same directed acyclic graph in Figure 3. We show in Supplementary material S2 that their decomposition resembles our proposed decomposition under certain conditions. Their CDE and
The expected values of our natural MI effects provide natural interpretations by accounting for the distributions of
Effect
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Definition | Interpretation |
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through both
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Due to the mediation through both
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Due to the mediation through
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Due to the mediation through
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3.2 Mediators causally sequential
In this section, we consider the scenario where the two mediators are causally sequential, i.e., there is a direct causal link from mediator
Definition 3
Natural MI effects in a causally sequential two-mediator scenario are defined as follows:
These interaction terms are similar to those in Definition 2 except that
We show in Supplementary material S3 that the TE can be decomposed into ten components at the individual level:
where
Since the complexity significantly increases in a sequential two-mediator scenario with a direct causal link pointing from
Second, the causal effect along the path
These ten components and their interpretations are shown in Table 3 for the special case when
Effect
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Definition | Interpretation |
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Due to neither mediation nor interaction |
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Due to the interaction between
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Due to the interaction between
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Due to the interaction between
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through both
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Due to the mediation through both
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Due to the mediation through
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Due to the partial mediation through
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4 Relations to traditional definitions
For both a non-sequential and a sequential two-mediator scenario, the ten components can be grouped into different portions with traditional definitions that are of great interest. In this section, we illustrate the relations of our proposed decompositions to the traditional definitions introduced in previous literature [7,16,17,21].
4.1 Non-sequential two-mediator scenario
Recall that the TE can be decomposed into the following ten components in a non-sequential two-mediator scenario:
First, the sum of the CDE and the reference interaction effects equals the PDE that evaluates the causal effect through the direct path
Intuitively, the CDE and the reference interaction effects are the only components in the decomposition that do not require any mediated effects to exist as shown in equation (1). The four-way decomposition [7] also has the corresponding relation but the reference interaction effect only consists of one term.
The TDE [16] is different from PDE in the way that the potential values
The natural MI effect between
The NIE through
where
The PIE through
since
The portion eliminated (PE) is another useful measure that evaluates how much the causal effect of the exposure on the outcome would be removed if the mediators were set to 0 [16,21]. It can be expressed as follows:
where the graphical illustration for this alternative decomposition with PE is shown in Figure 10.
If the components related to the effect due to interaction are of great interest, the portion attributable to interaction (PAI) [7] can be found by summing up the reference and natural MI effects. Namely, we have,
which leads to a four-way decomposition for a non-sequential two-mediator scenario:
Figure 11 presents an overall picture for the interaction and mediation decompositions with the ten components for a non-sequential two-mediator scenario. Suggested choices for the multiway interaction decompositions are summarized in Table 4.
Number of components |
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2-Way decomposition (no mediation) |
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4-Way decomposition |
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4-Way decomposition |
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5-Way decomposition |
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7-Way decomposition |
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10-Way decomposition |
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4.2 Sequential two-mediator scenario
We recall the ten components of the decomposed TE for a sequential two-mediator scenario:
As discussed in Section 3.2, the complete mediated effect through
5 Identification assumptions and empirical formulas
The decompositions for one- and two-mediator scenarios thus far have been primarily conceptual. The individual-level effects in the decompositions cannot be identified from data, but under certain assumptions on confounding the population-averages of those components can be identified from data [6].
5.1 Identification assumptions
We first consider a single-mediator scenario. Four identification assumptions are required [22], which are listed below as (
where
The analogs of (
Similarly, the assumptions above state that given a covariate set
In order to account for the confounding between
where (
Steen et al. [4] presented comprehensive identification assumptions for the causal structures with multiple mediators and pointed out that weaker identification assumptions than (
5.2 Empirical formulas
Suppose a set of covariates
When
5.3 Relations to linear models
Suppose
where
where
For a scenario with two causally non-sequential mediators, again we assume that a set of covariates
The results can be obtained as a special case of those derived from the scenario with two causally sequential mediators by setting parameters
where
Bellavia’s and Valeri’s method | Our proposed decomposition | ||
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Component
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Formula | Component
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Formula |
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Compared to
The key difference is that
6 Illustrations with simulated and real data
We use a simulated data set to compare our method to Bellavia’s and Valeri’s method [9] in a non-sequential two-mediator scenario. We also analyzed a real data set in a sequential two-mediator scenario using the formulas derived in Section 5.3 for illustration.
6.1 Illustration with a simulated data set in a non-sequential two-mediator scenario
To compare Bellavia’s and Valeri’s method and our proposed decomposition with two non-sequential mediators (Figure 3), we simulated
where the exposure
The covariate
The treatment and reference level of
Component
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True value | Estimate | 95% CI | Interpretation |
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0.3000 | 0.2891 | 0.2210, 0.3590 | Due to neither mediation nor interaction with fixed reference levels
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0.0024 | 0.0003 |
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Due to the interaction between
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0.0048 | 0.0101 | 0.0029, 0.0181 | Due to the interaction between
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0.0403 | 0.0332 | 0.0212, 0.0470 | Due to the interaction between
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PDE | 0.3475 | 0.3327 | 0.2647, 0.4012 | The causal effect through the direct path
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TE | 0.8707 | 0.8697 | 0.7841, 0.9561 | The overall causal effect of
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Bellavia’s and Valeri’s method | Our proposed decomposition | ||||||
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Component
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True value | Estimate | 95% CI | Component
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True value | Estimate | 95% CI |
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0.0030 | 0.0004 |
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0.0534 | 0.0439 | 0.0260, 0.0634 |
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0.0060 | 0.0165 | 0.0048, 0.0283 |
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0.0564 | 0.0703 | 0.0499, 0.0922 |
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0.1638 | 0.1680 | 0.1474, 0.1887 |
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0.0630 | 0.0706 | 0.0521, 0.0911 |
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0.1404 | 0.1286 | 0.1021, 0.1573 |
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0.0540 | 0.0541 | 0.0378, 0.0734 |
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0.0900 | 0.0902 | 0.0626, 0.1207 |
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0.1332 | 0.1236 | 0.0919, 0.1579 |
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0.1200 | 0.1333 | 0.1011, 0.1688 |
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0.1632 | 0.1745 | 0.1353, 0.2174 |
Bellavia’s and Valeri’s method has a few drawbacks. First of all, the mediated effects in Bellavia’s and Valeri’s method vary with respect to the arbitrary choices of
Bellavia’s and Valeri’s method | Our proposed decomposition | ||
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Component
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Interpretation | Component
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Interpretation |
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through both
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Due to the mediation through both
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Due to the mediation through both
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Due to the mediation through both
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through
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Due to the mediation through
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6.2 Illustration with real data in a sequential two-mediator scenario
6.2.1 Justification of the causal diagram
In our motivating example, we aim to examine the effect of alcohol consumption on hypertension, and the components of the TE that are due to the mediation or interaction with GGT and BMI. The hypothetical causal diagram with two sequential mediators is shown in Figure 12. We adopted the causal diagram from the study by Daniel et al. [3], and provided additional evidence from literature reports to support the causal diagram. While GGT is traditionally used as a biological marker for excessive alcohol consumption and liver function [25], it has been suggestive to be a robust marker for oxidative stress [26,27]. There is growing evidence that obesity, especially central obesity, may result in increased serum GGT levels [28,29]. Experimental and clinical studies have demonstrated the important role of GGT in antioxidant defense, detoxification, and inflammation processes [30]. There are a number of reports that have investigated the effects of GGT on the risk and prognosis of complex diseases such as cancer [31] and cardiovascular disease [32]. A study that has conducted a 12-week alcohol relapse prevention trial reported that participant with positive GGT (
To illustrate the concept of natural MI effect and the decomposition methods, we used the 2013–2014, 2015–2016, and 2017–2018 National Health and Nutrition Examination Survey data with 8,920 observations [3,39]. The data set was downloaded from http://www.cdc.gov/nhanes. Exposure
Log transformation was performed on GGT due to the skewness of the data. The fixed reference levels of
Table 9 presents the decomposition of the TE conditional on males and the mean level of age at 45.96. The CDE is 1.1014 (95% CI = 0.4900 to 1.7218); the reference interaction effect between
Component
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Estimate | 95% CI |
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1.1014 | 0.4900, 1.7218 |
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0.0329 |
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0.0745 |
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0.0025 |
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−0.0167 |
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0.1307 |
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0.0003 |
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−0.0059 |
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PDE | 1.2113 | 0.6011, 1.8326 |
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0.2137 | 0.0927, 0.3417 |
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0.3952 | 0.2581, 0.5470 |
TE | 1.9287 | 1.2874, 2.5807 |
Component
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Estimate | 95% CI |
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1.1014 | 0.4900, 1.7218 |
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−0.0097 |
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−0.2218 |
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0.0153 |
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−0.0195 |
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0.1312 |
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0.0003 |
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−0.0058 |
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PDE | 0.8853 | 0.2567, 1.5150 |
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0.2193 | 0.0949, 0.3512 |
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0.3852 | 0.2527, 0.5319 |
TE | 1.5960 | 0.9731, 2.2246 |
Overall, we observed a significant increase in SBP among heavy alcohol drinkers in both males (TE: 1.9287; 95% CI: 1.2874, 2.5807) and females (TE: 1.5960; 95% CI: 0.9731, 2.2246) compared to never/moderate drinkers. Detailed decomposition using our method showed that all three path effects (PDE,
7 Conclusion
In this work, we develop decompositions for scenarios where the two mediators are causally sequential or non-sequential. We propose a unified approach for decomposing the TE into components that are due to mediation only, interaction only, both mediation and interaction, and neither mediation nor interaction within the counterfactual framework. The decomposition was implemented via a new concept called natural MI effect that we proposed to describe the two-way and three-way interactions for both scenarios that extend the two-way MIs in existing literature. To estimate the components of our proposed decompositions, we lay out the identification assumptions. We also derive the formulas when the response is assumed to be continuous with linear structural equation models. We use both simulated and real data sets to illustrate our method.
We believe that our proposed new concept of natural MI effects and the decomposition methods for the causal framework with two sequential or non-sequential mediators provide a powerful tool to decipher the refined path effects while appropriately account for interaction effects among the exposure and mediators. The counterfactual interaction effects evaluate the interaction terms that involve mediators by treating them at the natural levels. There is a gap in existing research of decomposing TE into mediation and interaction effects for the scenario of multiple sequential mediators, and our proposed methods have the potential to fill in the gap. Our future work will include developing decomposition methods for causal structures involving multiple sequential mediators and multiple exposures. We will also investigate the interventional analogue version of this decomposition and the corresponding interpretation of the effects in the future work.
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Funding information: This research was partially supported by UNM Comprehensive Cancer Center Support Grant NCI P30CA118100, the Biostatistics shared resource, UNM METALS Superfund Research Center (NIEHS 1P42ES025589), and Center for Native American Environmental Health Equity Research (NIMHD/NIEHS 9P50MD015706).
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Conflict of interest: Authors state no conflict of interest.
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Data availability statement: The R scripts for the simulation study and real data analysis are available at: https://github.com/flourish-727/data_analysis.
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