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Mediation analysis is an important statistical tool in many research fields, where the joint significance test is widely utilized for examining mediation effects. Nevertheless, the limitation of this mediation testing method stems from its conservative Type I error, which reduces its statistical power and imposes certain constraints on its utility. The proposed solution to address this gap is the adjusted joint significance test for one mediator, which introduces a novel data-adjusted approach for assessing mediation effects that showcases significant advancements. The method is specifically designed to be user-friendly, thereby eliminating the necessity for intricate procedures. We further extend the adjusted joint significance test for small-scale mediation hypotheses with family-wise error rate (FWER) control. Additionally, a novel adjusted Sobel-type confidence interval is proposed for the mediation effects, demonstrating significant advancements over conventional Sobel's method. The effectiveness of our mediation testing and confidence interval estimation is assessed through extensive simulations, and compared against a multitude of existing approaches. Finally, we present the application of the method to three substantive datasets with continuous, binary and time-to-event outcomes, respectively. [ABSTRACT FROM AUTHOR]

This study proposes a heterogeneous mediation analysis for survival data that accommodates multiple mediators and sparsity of the predictors. We introduce a joint modeling approach that links the mediation regression and proportional hazards models through Bayesian additive regression trees with shared typologies. The shared tree component is motivated by the fact that confounders and effect modifiers on the causal pathways linked by different mediators often overlap. A sparsity‐inducing prior is incorporated to capture the most relevant confounders and effect modifiers on different causal pathways. The individual‐specific interventional direct and indirect effects are derived on the scale of the logarithm of hazards and survival function. A Bayesian approach with an efficient Markov chain Monte Carlo algorithm is developed to estimate the conditional interventional effects through the Monte Carlo implementation of the mediation formula. Simulation studies are conducted to verify the empirical performance of the proposed method. An application to the ACTG175 study further demonstrates the method's utility in causal discovery and heterogeneity quantification. [ABSTRACT FROM AUTHOR]

Background: The mechanistic associations between obesity and risk of colorectal cancer (CRC) remain unclear. Here, using body mass index (BMI) as an obesity indicator, we decomposed the total effects of obesity on the risk of CRC into: (1) direct effects, which are possibly mediated by unmeasured or currently unknown factors; (2) indirect effects mediated by circulating leptin and adiponectin; and (3) indirect effects that are not mediated by circulating leptin and adiponectin but by hyperinsulinemia and chronic inflammation (assessed via circulating connecting peptide and C-reactive protein, respectively). Methods: We adopted a causal mediation framework, using data from a large prospective cohort study of 44,271 Japanese men. Results: BMI was not associated with the risk of CRC due to direct and indirect effects that were not mediated by circulating leptin and adiponectin. By contrast, individuals with BMIs of 25.0-27.4 kg/m2 (risk ratio 1.29; 95% confidence interval, 0.98-1.69) and ≥27.5 kg/m2 (risk ratio 1.28; 95% confidence interval, 0.98-1.68) had a higher risk of CRC due to indirect effects of circulating leptin and adiponectin. Conclusion: Our mediation analyses suggest that the association between BMI and CRC risk may be largely mediated by a pathway involving circulating leptin and adiponectin. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this research was to investigate the association between leisure activity and cognitive function in Chinese older adults, and to explore the mediating role of individual evaluation, anxiety and depression. Using cross-sectional data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), Pearson correlation analyses were employed to examine the relationships among various indicators. A multiple mediation model was constructed to investigate the mediating pathways through which leisure activities impact cognitive function in elderly individuals. Leisure activities, individual evaluation, anxiety, and depression were significantly correlated with cognitive function (p

The purpose of this research was to investigate the association between leisure activity and cognitive function in Chinese older adults, and to explore the mediating role of individual evaluation, anxiety and depression. Using cross-sectional data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), Pearson correlation analyses were employed to examine the relationships among various indicators. A multiple mediation model was constructed to investigate the mediating pathways through which leisure activities impact cognitive function in elderly individuals. Leisure activities, individual evaluation, anxiety, and depression were significantly correlated with cognitive function (p < 0.01). Leisure activities not only directly positively impacted cognitive function in elderly individuals (effect = 0.3780; 95% CI = [0.3459, 0.4101]), but also exerted indirect effects through five pathways. Individual evaluation, anxiety, depression, they plays a mediating role in the association between leisure activity and cognitive function in older adults in China. Leisure activities can positively impact cognitive function in elderly individuals by enhancing individual evaluation and subsequently reducing anxiety and depression. [ABSTRACT FROM AUTHOR]

Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data (outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture with three levels. We use standardization (g-computation) to compute all possible mediation effects, including pairwise and all other possible interaction among the mediators. We thoroughly explore our method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study, where we estimate how the effect of births from unintended pregnancies on later life mental depression (CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment instability, lack of social participation, and increased family stress. Our method identified significant individual mediators, along with some significant pairwise effects. [ABSTRACT FROM AUTHOR]

Sequencing read-count data often exhibit sparsity (zero-count inflation) and overdispersion. As most sequencing techniques provide an arbitrary total count, taxon-specific counts should be treated under the compositional data-analytic framework. There is increasing interest in the role of gut microbiome composition in mediating the effects of exposures on health. Previous compositional mediation approaches have focused on identifying mediating taxa among a number of candidates. We here consider compositional causal mediation when a priori knowledge is available about the hierarchy for a restricted number of taxa, building on a single hypothesis structured as contrasts between appropriate sub-compositions. Based on the assumed causal graph and the theory of multiple contemporaneous mediators, we define non-parametric estimands for overall and coordinate-wise mediation effects and show how they are estimated based on parametric linear models. The mediators have straightforward and coherent interpretations, related to causal questions about interrelationships between the sub-compositions. We perform a simulation study focusing on the impact of sparsity on estimation. While unbiased, the estimators' precision depends on sparsity and the relative magnitudes of exposure-to-mediator and mediator-to-outcome effects in a complex manner. In the empirical application we find an inverse association of fibre intake on insulin level, mainly attributable to the direct effects. [ABSTRACT FROM AUTHOR]

Background: In the current social environment, scarcity, as a universally present objective state, profoundly impacts individuals' decision-making and health through the subjective feeling it induces, known as a "scarcity mindset." Particularly, the feeling of scarcity related to money and sleep time is not only widespread but also directly linked to an individual's mental health. Purpose: This study aims to delve into the relationship between the feeling of scarcity and mental health, with a specific focus on the relationship between the feeling of money scarcity or sleep time scarcity and mental health, as well as the role of sleep quality or life satisfaction in this relationship. Procedure: We determined the sampling quotas based on the population and economic development levels of each province in the "China Statistical Yearbook (2021)" (National Bureau of Statistics, 2021). Participants were selected using the Probability Proportional to Size (PPS) sampling method. Data was collected by distributing online questionnaires to participants, and the relationships between the main variables were explored using structural equation modeling. Results: 1. In China, the feeling of sleep time scarcity is stronger than the feeling of money scarcity among the public. 2. The feeling of money scarcity is positively correlated with depression and anxiety, whereas the feeling of sleep time scarcity is only positively correlated with depression. 3. The feeling of scarcity mainly leads to depression and self-denial through reducing life satisfaction, and it leads to anxiety through reducing sleep quality. Conclusion: The feeling of scarcity in money and sleep time is related to different dimensions of mental health. Therefore, reasonably planning financial allocation and ensuring an adequate amount of sleep can reduce the sense of scarcity, thereby improving mental health. Additionally, improving sleep quality and increasing life satisfaction can alleviate the adverse effects of scarcity on mental health. [ABSTRACT FROM AUTHOR]

Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation analysis for an ordinal outcome are still underdeveloped. In this paper, we first review mediation analysis methods with a continuous mediator for an ordinal outcome and then develop mediation analysis with a binary mediator for an ordinal outcome. We further consider multiple mediators for an ordinal outcome in the counterfactual framework and provide identification assumptions for identifying the mediation effects. Under the identification assumptions, we propose a regression-based method to estimate the mediation effects through multiple mediators while allowing the presence of exposure-mediator interactions. The closed-form expressions of mediation effects are also obtained for three scenarios: multiple continuous mediators, multiple binary mediators, and multiple mixed mediators. We conduct simulation studies to assess the finite sample performance of our new methods and present the biases, standard errors, and confidence intervals to demonstrate that our proposed estimators perform well in a wide range of practical settings. Finally, we apply our proposed methods to assess the mediation effects of candidate DNA methylation CpG sites in the causal pathway from socioeconomic index to body mass index. [ABSTRACT FROM AUTHOR]

The decomposition of the overall effect of a treatment into direct and indirect effects is here investigated with reference to a recursive system of binary random variables. We show how, for the single mediator context, the marginal effect measured on the log odds scale can be written as the sum of the indirect and direct effects plus a residual term that vanishes under some specific conditions. We then extend our definitions to situations involving multiple mediators and address research questions concerning the decomposition of the total effect when some mediators on the pathway from the treatment to the outcome are marginalized over. Connections to the counterfactual definitions of the effects are also made. Data coming from an encouragement design on students' attitude to visit museums in Florence, Italy, are reanalyzed. The estimates of the defined quantities are reported together with their standard errors to compute p values and form confidence intervals. [ABSTRACT FROM AUTHOR]

Pain neuroscience education combined with exercise (PNE 1 exercise) is an effective treatment for patients with chronic spinal pain. Yet, however, little is known about its underlying therapeutic mechanisms. Thus, this study aimed to provide the first insights by performing a novel mediation analysis approach in a published randomized controlled trial in primary care where PNE 1 exercise was compared with standard physiotherapy. Four mediators (catastrophizing, kinesiophobia, central sensitization-related distress, and pain intensity) measured at postintervention and 3 outcomes (disability, health-related quality of life, and pain medication intake) measured at 6-month follow-up were included into the analysis. The postintervention measure of each outcome was also introduced as a competing candidate mediator in each respective model. In addition, we repeated the analysis by including all pairwise mediator-mediator interactions to allow the effect of each mediator to differ based on the other mediators' values. Postintervention improvements in disability, medication intake, and health-related quality of life strongly mediated PNE 1 exercise effects on each of these outcomes at 6-month follow-up, respectively. Reductions in disability and medication intake were also mediated by reductions in kinesiophobia and central sensitization-related distress. Reductions in kinesiophobia also mediated gains in the quality of life. Changes in catastrophizing and pain intensity did not mediate improvements in any outcome. The mediation analyses with mediator-mediator interactions suggested a potential effect modification rather than causal independence among the mediators. The current results, therefore, support the PNE framework to some extent as well as highlight the need for implementing the recent approaches for mediation analysis to accommodate dependencies among the mediators. [ABSTRACT FROM AUTHOR]

Background: Fatty liver, obesity, and dyslipidemia are associated with prediabetes or diabetes risk, and hyperuricemia co-exists. The present study evaluated the role of multiple mediators, namely, fatty liver, body mass index (BMI), and dyslipidemia, in the association between hyperuricemia and diabetes status. Methods: Baseline data from the ongoing Fuqing cohort (5,336 participants) were analyzed to investigate the association of hyperuricemia with diabetes status using a multinomial logistic regression model. Furthermore, causal mediation analysis with the weighting-based approach was performed to estimate hyperuricemia's total natural direct effect (tnde), total natural indirect effect (tnie), and total effect (te) on prediabetes and diabetes risk, mediating jointly via fatty liver, BMI, and dyslipidemia. Results: In multinomial analysis without considering mediators' effects, hyperuricemia was associated with a higher risk of prediabetes only (odds ratio: 1.25; 95% CI: 1.09-1.43; p < 0.001). When fatty liver, BMI, and dyslipidemia were considered as multiple mediators in the association, hyperuricemia was linked to both prediabetes [tnde: 1.11, 95% CI: 1.04-1.11; tnie: 1.07, 95% CI: 1.05-1.09; and overall proportion mediated (pm): 42%, 95% CI: 27%-73%] and diabetes risk (tnde: 0.96, 95% CI: 0.82-1.14; tnie: 1.25, 95% CI: 1.18-1.33; and pm: 100%, 95% CI: 57%-361%). Hyperuricemia showed significant tnde, te, and tnie, mediated by fatty liver jointly with dyslipidemia (pm = 17%) or BMI (pm = 35%), on prediabetes risk. Conclusion: Hyperuricemia could increase prediabetes or diabetes risk, partially mediated by fatty liver, BMI, and dyslipidemia. Fatty liver is the crucial mediator in the association between hyperuricemia and prediabetes. [ABSTRACT FROM AUTHOR]

It is often of interest in the health and social sciences to investigate the joint mediation effects of multiple post‐exposure mediating variables. Identification of such joint mediation effects generally require no unmeasured confounding of the outcome with respect to the whole set of mediators. As the number of mediators under consideration grows, this key assumption is likely to be violated as it is often infeasible to intervene on any of the mediators. In this article, we develop a simple two‐step method of moments estimation procedure to assess mediation with multiple mediators simultaneously in the presence of potential unmeasured mediator‐outcome confounding. Our identification result leverages heterogeneity of the population exposure effect on the mediators, which is plausible under a variety of empirical settings. The proposed estimators are illustrated through both simulations and an application to evaluate the mediating effects of post‐traumatic stress disorder symptoms in the association between self‐efficacy and fatigue among health care workers during the COVID‐19 outbreak. [ABSTRACT FROM AUTHOR]

BackgroundFatty liver, obesity, and dyslipidemia are associated with prediabetes or diabetes risk, and hyperuricemia co-exists. The present study evaluated the role of multiple mediators, namely, fatty liver, body mass index (BMI), and dyslipidemia, in the association between hyperuricemia and diabetes status.MethodsBaseline data from the ongoing Fuqing cohort (5,336 participants) were analyzed to investigate the association of hyperuricemia with diabetes status using a multinomial logistic regression model. Furthermore, causal mediation analysis with the weighting-based approach was performed to estimate hyperuricemia’s total natural direct effect (tnde), total natural indirect effect (tnie), and total effect (te) on prediabetes and diabetes risk, mediating jointly via fatty liver, BMI, and dyslipidemia.ResultsIn multinomial analysis without considering mediators’ effects, hyperuricemia was associated with a higher risk of prediabetes only (odds ratio: 1.25; 95% CI: 1.09–1.43; p < 0.001). When fatty liver, BMI, and dyslipidemia were considered as multiple mediators in the association, hyperuricemia was linked to both prediabetes [tnde: 1.11, 95% CI: 1.04–1.11; tnie: 1.07, 95% CI: 1.05–1.09; and overall proportion mediated (pm): 42%, 95% CI: 27%–73%] and diabetes risk (tnde: 0.96, 95% CI: 0.82–1.14; tnie: 1.25, 95% CI: 1.18–1.33; and pm: 100%, 95% CI: 57%–361%). Hyperuricemia showed significant tnde, te, and tnie, mediated by fatty liver jointly with dyslipidemia (pm = 17%) or BMI (pm = 35%), on prediabetes risk.ConclusionHyperuricemia could increase prediabetes or diabetes risk, partially mediated by fatty liver, BMI, and dyslipidemia. Fatty liver is the crucial mediator in the association between hyperuricemia and prediabetes.

Randomized experiments are often complicated because of treatment noncompliance. This challenge prevents researchers from identifying the mediated portion of the intention-to-treated (ITT) effect, which is the effect of the assigned treatment that is attributed to a mediator. One solution suggests identifying the mediated ITT effect on the basis of the average causal mediation effect among compliers when there is a single mediator. However, considering the complex nature of the mediating mechanisms, it is natural to assume that there are multiple variables that mediate through the causal path. Motivated by an empirical analysis of a data set collected in a randomized interventional study, we develop a method to estimate the mediated portion of the ITT effect when both multiple dependent mediators and treatment noncompliance exist. This enables researchers to make an informed decision on how to strengthen the intervention effect by identifying relevant mediators despite treatment noncompliance. We propose a nonparametric estimation procedure and provide a sensitivity analysis for key assumptions. We conduct a Monte Carlo simulation study to assess the finite sample performance of the proposed approach. The proposed method is illustrated by an empirical analysis of JOBS II data, in which a job training intervention was used to prevent mental health deterioration among unemployed individuals.

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. We implemented a parametric method, along with a user-friendly algorithm, in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms accounted for 14.6% of the total effect and that mediated by physical activity accounted for 11.9%. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status. See video abstract at, http://links.lww.com/EDE/B988 . [ABSTRACT FROM AUTHOR]

This study examined the relationships among school principals' distributed leadership, teachers' psychological capital, trust in the principal, work engagement, and turnover intention. The research sample consisted of 397 primary and secondary school teachers. The study employed a cross-sectional survey design using quantitative methods. Distributed Leadership Scale, Psychological Capital Questionnaire-Short Form, Utrecht Work Engagement Scale-Ultra short version, Trust in the Principal Scale, and Teachers' Intent to Move to Another School Scale were used to gather the data. Multiple mediator analysis in structural equation modeling (SEM) with the bootstrapping method was used to analyze the direct and indirect effects of dependent variables on teachers' turnover intentions. 95 % confidence intervals were calculated using 2000 samples to examine the mediating effects. The results indicated that the direct effects of distributed leadership and psychological capital on teachers' turnover intentions are insignificant. Psychological capital negatively and indirectly affects turnover intentions through work engagement while distributed leadership negatively and indirectly influences turnover intentions via work engagement and trust in principal. The study emphasized the role of job and personal resources in understanding teachers' turnover intentions. Based on the results, principals are recommended to demonstrate distributed leadership behaviors to build trust and use intervention strategies to strengthen teachers' psychological capacities if they want to lower teachers' turnover intentions. [ABSTRACT FROM AUTHOR]

Natural mediation effects are desirable estimands for studying causal mechanisms in a population, but complications arise in defining and estimating natural indirect effects through multiple mediators with an unspecified causal ordering. We propose a decomposition of the natural indirect effect of multiple mediators into individual components, termed exit indirect effects, and a remainder interaction term, and study the similarities to and differences from existing natural and interventional effects in the literature. We provide a set of identification assumptions for estimating all components of the proposed natural effect decomposition and derive the semiparametric efficiency bounds for the effects. The efficient influence functions contain conditional densities that are variationally dependent, which is uncommon in existing problems and may lead to model incompatibility. By ensuring model compatibility through a reparameterization based on copulas, our estimator is quadruply robust, which means that it remains consistent and asymptotically normal under four types of possible misspecification, and also is locally semiparametric efficient. We further propose a stabilized quadruply robust estimator to improve practical performance under possibly misspecified models, as well as a nonparametric extension based on sample splitting. [ABSTRACT FROM AUTHOR]

Background: Causal mediation analysis facilitates decomposing the total effect into a direct effect and an indirect effect that operates through an intermediate variable. Recent developments in causal mediation analysis have clarified the process of evaluating how-and to what extent-different pathways via multiple causally ordered mediators link the exposure to the outcome.Methods: Through an application of natural effect models for multiple mediators, we show how placental abruption might affect perinatal mortality using small for gestational age (SGA) birth and preterm delivery as two sequential mediators. We describe methods to disentangle the total effect into the proportions mediated via each of the sequential mediators, when evaluating natural direct and natural indirect effects.Results: Under the assumption that SGA births causally precedes preterm delivery, an analysis of 16.7 million singleton pregnancies is consistent with the hypothesis that abruption exerts powerful effects on perinatal mortality (adjusted risk ratio = 11.9; 95% confidence interval = 11.6, 12.1). The proportions of the estimated total effect mediated through SGA birth and preterm delivery were 2% and 58%, respectively. The proportion unmediated via either SGA or preterm delivery was 41%.Conclusions: Through an application of causal mediation analysis with sequential mediators, we uncovered new insights into the pathways along which abruption impacts perinatal mortality. [ABSTRACT FROM AUTHOR]

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

Causal mediation analysis investigates the mechanism linking exposure and outcome. Dealing with the impact of unobserved confounders among exposure, mediator and outcome is an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, rendering it difficult to estimate the path-specific effects. In this study, we propose a method (PSE-MR) to identify and estimate path-specific effects of an exposure (e.g. education) on an outcome (e.g. osteoarthritis risk) through multiple causally ordered and non-ordered mediators (e.g. body mass index and pack-years of smoking) using summarized genetic data, when the sequential ignorability assumption is violated. Specifically, PSE-MR requires a specific rank condition in which the number of instrumental variables is larger than the number of mediators. Furthermore, we illustrate the utility of PSE-MR by providing guidance for practitioners and exploring the mediation effects of body mass index and pack-years of smoking in the causal pathways from education to osteoarthritis risk. Additionally, the results of simulation reveal that the causal estimates of path-specific effects are almost unbiased with good coverage and Type I error properties. Also, we summarize the least number of instrumental variables for the specific number of mediators to achieve 80% power. [ABSTRACT FROM AUTHOR]

Regression-based approaches are widely used in causal mediation analysis. The presence of multiple mediators, however, increases the complexity and difficulty of mediation analysis. In such cases, regression-based approaches cannot efficiently address estimation issues. Hence, a flexible approach to mediation analysis is needed. Therefore, we developed a method for using g-computation algorithm to conduct causal mediation analysis in the presence of multiple ordered mediators. Compared to regression-based approaches, the proposed simulation-based approach increases flexibility in the choice of models and increases the range of the outcome scale. The Taiwanese Cohort Study dataset was used to evaluate the efficacy of the proposed approach for investigating the mediating role of early and late HBV viral load in the effect of HCV infection on hepatocellular carcinoma (HCC) in HBV seropositive patients (n = 2,878; HCV carrier n = 123). Our results indicated that early HBV viral load had a negative mediating role in HCV-induced HCC. Additionally, early exposure to a low HBV viral load affected HCC through a lag effect on HCC incidence [OR = 0.873, 95% CI = (0.853, 0.893)], and the effect of early exposure to a low HBV viral load on HCC incidence was slightly larger than that of a persistently low viral load on HCC incidence [OR = 0.918, 95% CI = (0.896, 0.941)].

Mediation analysis aims at disentangling the effects of a treatment on an outcome through alternative causal mechanisms and has become a popular practice in biomedical and social science applications. The causal framework based on counterfactuals is currently the standard approach to mediation, with important methodological advances introduced in the literature in the last decade, especially for simple mediation, that is with one mediator at the time. Among a variety of alternative approaches, Imai et al. showed theoretical results and developed an R package to deal with simple mediation as well as with multiple mediation involving multiple mediators conditionally independent given the treatment and baseline covariates. This approach does not allow to consider the often encountered situation in which an unobserved common cause induces a spurious correlation between the mediators. In this context, which we refer to as mediation with uncausally related mediators, we show that, under appropriate hypothesis, the natural direct and joint indirect effects are non-parametrically identifiable. Moreover, we adopt the quasi-Bayesian algorithm developed by Imai et al. and propose a procedure based on the simulation of counterfactual distributions to estimate not only the direct and joint indirect effects but also the indirect effects through individual mediators. We study the properties of the proposed estimators through simulations. As an illustration, we apply our method on a real data set from a large cohort to assess the effect of hormone replacement treatment on breast cancer risk through three mediators, namely dense mammographic area, nondense area and body mass index. [ABSTRACT FROM AUTHOR]

Neighbourhood safety represents an important topic of study to illustrate the reasons behind the increases in crime and mitigate its effects in neighbourhoods. This study examines how the social and environmental features of neighbourhoods may influence the social sustainability of residents based on the assumption that the perception of safety and social cohesion mediates the effects of neighbourhood environment on social sustainability. A quantitative method was employed to collect data from residents in a low-rise residential area in Penang, Malaysia. The results of structural equation modelling (SEM) indicated the positive and significant effect of neighbourhood accessibility on perceived disorder, whilst the effect of accessibility on social cohesion was negative. Disorders may comprise social and physical disorders, and may have a negative effect on perception of safety, but not on social cohesion. The relationship between disorders and social sustainability is serially mediated by the perception of safety and social cohesion. This implies that those who perceived high disorderliness in a neighbourhood environment reported a lower level of perception of safety, social cohesion and lower levels of social sustainability. Attempts need to be made to reduce neighbourhood disorderliness to pave the way for 2030 Agenda goals implementation. [ABSTRACT FROM AUTHOR]

Abstract Background Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators. Results We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators. Conclusions Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators' distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future. [ABSTRACT FROM AUTHOR]

This paper demonstrates the utility of latent classes in evaluating the effect of an intervention on an outcome through multiple indicators of mediation. These indicators are observed intermediate variables that identify an underlying latent class mediator, with each class representing a different mediating pathway. The use of a latent class mediator allows us to avoid modeling the complex interactions between the multiple indicators and ensures the decomposition of the total mediating effects into additive effects from individual mediating pathways, a desirable feature for evaluating multiple indicators of mediation. This method is suitable when the goal is to estimate the total mediating effects that can be decomposed into the additive effects of distinct mediating pathways. Each indicator may be involved in multiple mediation pathways and at the same time multiple indicators may contribute to a single mediating pathway. The relative importance of each pathway may vary across subjects. We applied this method to the analysis of the first 6 months of data from a 2-year clustered randomized trial for adults in their first episode of schizophrenia. Four indicators of mediation are considered: individual resiliency training; family psychoeducation; supported education and employment; and a structural assessment for medication. The improvement in symptoms was found to be mediated by the latent class mediator derived from these four service indicators. Simulation studies were conducted to assess the performance of the proposed model and showed that the simultaneous estimation through the maximum likelihood yielded little bias when the entropy of the indicators was high. [ABSTRACT FROM AUTHOR]

Drawing on a life course and stress process perspective, this study examined the pathway from childhood SES to later year depressive symptoms, focusing on multiple life course SES and coping resources in old age. Data came from the 2006 and 2015 waves of the Korea Welfare Panel Study (KOWEPS). We selected respondents aged 51–55 in 2006 who were followed up when they were between 60 to 64 years of age in 2015. We merged the middle aged data in 2006 with the 2015 data when the respondents were 60–64 years of age (N = 687). Phantom modeling was used to examine a multiple mediation pathway and multi-group analyses were conducted to examine the gender differences in the pathway. For older men, satisfaction with social/leisure activities was a significant coping resource, while, for older women, satisfaction with family relations was important. [ABSTRACT FROM AUTHOR]

Background: Socio-economic inequalities in mortality are well established, yet the contribution of intermediate risk factors that may underlie these relationships remains unclear. We evaluated the role of multiple modifiable intermediate risk factors underlying socio-economic-associated mortality and quantified the potential impact of reducing early all-cause mortality by hypothetically altering socio-economic risk factors.Methods: Data were from seven cohort studies participating in the LIFEPATH Consortium (total n = 179 090). Using both socio-economic position (SEP) (based on occupation) and education, we estimated the natural direct effect on all-cause mortality and the natural indirect effect via the joint mediating role of smoking, alcohol intake, dietary patterns, physical activity, body mass index, hypertension, diabetes and coronary artery disease. Hazard ratios (HRs) were estimated, using counterfactual natural effect models under different hypothetical actions of either lower or higher SEP or education.Results: Lower SEP and education were associated with an increase in all-cause mortality within an average follow-up time of 17.5 years. Mortality was reduced via modelled hypothetical actions of increasing SEP or education. Through higher education, the HR was 0.85 [95% confidence interval (CI) 0.84, 0.86] for women and 0.71 (95% CI 0.70, 0.74) for men, compared with lower education. In addition, 34% and 38% of the effect was jointly mediated for women and men, respectively. The benefits from altering SEP were slightly more modest.Conclusions: These observational findings support policies to reduce mortality both through improving socio-economic circumstances and increasing education, and by altering intermediaries, such as lifestyle behaviours and morbidities. [ABSTRACT FROM AUTHOR]

Neighbourhood safety represents an important topic of study to illustrate the reasons behind the increases in crime and mitigate its effects in neighbourhoods. This study examines how the social and environmental features of neighbourhoods may influence the social sustainability of residents based on the assumption that the perception of safety and social cohesion mediates the effects of neighbourhood environment on social sustainability. A quantitative method was employed to collect data from residents in a low-rise residential area in Penang, Malaysia. The results of structural equation modelling (SEM) indicated the positive and significant effect of neighbourhood accessibility on perceived disorder, whilst the effect of accessibility on social cohesion was negative. Disorders may comprise social and physical disorders, and may have a negative effect on perception of safety, but not on social cohesion. The relationship between disorders and social sustainability is serially mediated by the perception of safety and social cohesion. This implies that those who perceived high disorderliness in a neighbourhood environment reported a lower level of perception of safety, social cohesion and lower levels of social sustainability. Attempts need to be made to reduce neighbourhood disorderliness to pave the way for 2030 Agenda goals implementation.

Causal mediation analysis provides investigators insight into how a treatment or exposure can affect an outcome of interest through one or more mediators on causal pathway. When multiple mediators on the pathway are causally ordered, identification of mediation effects on certain causal pathways requires a sensitivity parameter to be specified. A mixed model-based approach was proposed in the Bayesian framework to connect potential outcomes at different treatment levels, and identify mediation effects independent of a sensitivity parameter, for the natural direct and indirect effects on all causal pathways. The proposed method is illustrated in a linear setting for mediators and outcome, with mediator-treatment interactions. Sensitivity analysis was performed for the prior choices in the Bayesian models. The proposed Bayesian method was applied to an adolescent dental health study, to see how social economic status can affect dental caries through a sequence of causally ordered mediators in dental visit and oral hygiene index. [ABSTRACT FROM AUTHOR]

We address the problem of testing whether a possibly high-dimensional vector may act as a mediator between some exposure variable and the outcome of interest. We propose a global test for mediation, which combines a global test with the intersection-union principle. We discuss theoretical properties of our approach and conduct simulation studies that demonstrate that it performs equally well or better than its competitor. We also propose a multiple testing procedure, ScreenMin, that provides asymptotic control of either familywise error rate or false discovery rate when multiple groups of potential mediators are tested simultaneously. We apply our approach to data from a large Norwegian cohort study, where we look at the hypothesis that smoking increases the risk of lung cancer by modifying the level of DNA methylation. [ABSTRACT FROM AUTHOR]

Causal mediation analysis aims to investigate the mechanism linking an exposure and an outcome. However, studies regarding mediation effects on survival outcomes are limited, particularly in multi-mediator settings. The existing multi-mediator analyses for survival outcomes are either performed under special model specifications such as probit models or additive hazard models, or they assume a rare outcome. Here, we propose a novel multi-mediation analysis based on the widely used Cox proportional hazards model without the rare outcome assumption. We develop a methodology under a counterfactual framework to identify path-specific effects (PSEs) of the exposure on the outcome through the mediator(s) and derive the closed-form formula for PSEs on a transformed survival time. Moreover, we show that the convolution of an extreme value and Gaussian random variables converges to another Gaussian, provided that the variance of the original Gaussian gets large. Based on that, we further derive closed-form expressions for PSEs on survival probabilities. Asymptotic properties are established for both estimators. Extensive simulation is conducted to evaluate the finite sample performance of our proposed estimators and to compare with existing methods. The utility of the proposed method is illustrated in a hepatitis study of liver cancer risk. [ABSTRACT FROM AUTHOR]

Background: Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators. Results: We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators. Conclusions: Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples. [ABSTRACT FROM AUTHOR]

This study investigates the relationships among service experience, emotions, perceived value, satisfaction and behavioral intentions in a wellness spa service context. This paper focuses on the mediating effects of emotions, perceived value, and satisfaction on the relationship between service experience and behavioral intentions; no previous study has examined the complete mediating relationship among these variables. Data were collected through a survey of 500 Thai day spagoers in five major cities in Thailand. The results show that emotions, perceived value, and satisfaction sequentially mediate the relationship between service experience and behavioral intentions; however, the direct effects are nonsignificant, indicating full mediation. Significant relationships were found among service experience, emotions, perceived value, satisfaction and behavioral intentions, providing a more holistic view of Thai spagoers’ decision-making process. This paper presents the theoretical and practical implications of these findings. [ABSTRACT FROM AUTHOR]

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. [ABSTRACT FROM AUTHOR]

The causal mediation literature has mainly focused on "natural effects" as measures of mediation, but these have been criticized for their reliance on empirically unverifiable assumptions. They are also impossible to estimate without additional untestable assumptions in the common situation of exposure-induced mediator-outcome confounding. "Interventional effects" have been proposed as alternative measures that overcome these limitations, and 2 versions have been described for the exposure-induced confounding problem. We aim to provide insight into the interpretation of these effects, particularly by describing randomized controlled trials that could hypothetically be conducted to estimate them. In contrast with natural effects, which are defined in terms of individual-level interventions, the definitions of interventional effects rely on population-level interventions. This distinction underpins the previously described advantages of interventional effects, and reflects a shift from individual effects to more tangible population-average effects. We discuss the conceptual and practical implications for the conduct of mediation analysis. See video abstract at, http://links.lww.com/EDE/B383. [ABSTRACT FROM AUTHOR]

Background: Approximately twice as many females as males are diagnosed with posttraumatic stress disorder (PTSD). However, little is known about why females report more PTSD symptoms than males. Prior studies have generally focused on few potential mediators at a time and have often used methods that were not ideally suited to test for mediation effects. Prior research has identified a number of individual risk factors that may contribute to sex differences in PTSD severity, although these cannot fully account for the increased symptom levels in females when examined individually. Objective: The present study is the first to systematically test the hypothesis that a combination of pre-, peri-, and posttraumatic risk factors more prevalent in females can account for sex differences in PTSD severity. Method: The study was a quasi-prospective questionnaire survey assessing PTSD and related variables in 73.3% of all Danish bank employees exposed to bank robbery during the period from April 2010 to April 2011. Participants filled out questionnaires 1 week (T1, N=450) and 6 months after the robbery (T2, N=368; 61.1% females). Mediation was examined using an analysis designed specifically to test a multiple mediator model. Results: Females reported more PTSD symptoms than males and higher levels of neuroticism, depression, physical anxiety sensitivity, peritraumatic fear, horror, and helplessness (the A2 criterion), tonic immobility, panic, dissociation, negative posttraumatic cognitions about self and the world, and feeling let down. These variables were included in the model as potential mediators. The combination of risk factors significantly mediated the association between sex and PTSD severity, accounting for 83% of the association. Conclusions: The findings suggest that females report more PTSD symptoms because they experience higher levels of associated risk factors. The results are relevant to other trauma populations and to other trauma-related psychiatric disorders more prevalent in females, such as depression and anxiety.

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries. [ABSTRACT FROM AUTHOR]

The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered fromobserved data in such complex settings.We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of theWorld Health Organization's Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect ofmold exposure onmental health (2002-2003). [ABSTRACT FROM AUTHOR]

Objectives:The main aim was to explore the multiple mediation effects of personal resources (mastery, engagement and disengagement coping strategies) between caregiving burden and depression in spousal caregivers, based on integrated stress process models. A further aim was to examine whether emotional suppression moderates the relationship between perceived burden and depression. Method:Cross-sectional data were collected in 2010–2011 by structured interviews from a sample of 110 spousal caregivers. Coefficients strategy with bootstrapping tested the strength and significance of the conditional indirect effects of simultaneous multiple mediators; and the conditional effect of burden on depression at different emotional suppression values. Results:Caregiving burdenwas indirectly associated with depression, through mastery and disengagement coping, but not through engagement coping. Mastery was negatively associated with depression, while disengagement coping was positively associated with depression. Emotional suppression and functional disability were positively associated with depressive symptoms. A significant burden-by-emotional suppression interaction was found for predicting depressive symptoms. Conclusion:The study supports the conditional indirect effect in which burden can affect depression by reducing the mastery and increasing the disengagement coping simultaneously. The study provides also initial support for the moderating effect of emotional suppression, which can increase the deleterious effects of burden on depression. Researchers and practitioners should be aware of these issues of family care in spousal caregivers. [ABSTRACT FROM PUBLISHER]

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: (a) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, (b) predict potential outcome probabilities, and (c) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance and covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the maximum likelihood (ML), mean-and-variance-adjusted weighted least squares (WLSMV) and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms ML/WLSMV regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration. [ABSTRACT FROM AUTHOR]

Background: Approximately twice as many females as males are diagnosed with posttraumatic stress disorder (PTSD). However, little is known about why females report more PTSD symptoms than males. Prior studies have generally focused on few potential mediators at a time and have often used methods that were not ideally suited to test for mediation effects. Prior research has identified a number of individual risk factors that may contribute to sex differences in PTSD severity, although these cannot fully account for the increased symptom levels in females when examined individually. Objective: The present study is the first to systematically test the hypothesis that a combination of pre-, peri-, and posttraumatic risk factors more prevalent in females can account for sex differences in PTSD severity. Method: The study was a quasi-prospective questionnaire survey assessing PTSD and related variables in 73.3% of all Danish bank employees exposed to bank robbery during the period from April 2010 to April 2011. Participants filled out questionnaires 1 week (T1, N=450) and 6 months after the robbery (T2, N=368; 61.1% females). Mediation was examined using an analysis designed specifically to test a multiple mediator model. Results: Females reported more PTSD symptoms than males and higher levels of neuroticism, depression, physical anxiety sensitivity, peritraumatic fear, horror, and helplessness (the A2 criterion), tonic immobility, panic, dissociation, negative posttraumatic cognitions about self and the world, and feeling let down. These variables were included in the model as potential mediators. The combination of risk factors significantly mediated the association between sex and PTSD severity, accounting for 83% of the association. Conclusion: The findings suggest that females report more PTSD symptoms because they experience higher levels of associated risk factors. The results are relevant to other trauma populations and to other traumarelated psychiatric disorders more prevalent in females, such as depression and anxiety. [ABSTRACT FROM AUTHOR]