Standard Mediation Analysis Research Articles

Depression, stress, and anxiety versus internet addiction in early and middle adolescent groups: the mediating roles of family and school environments

BackgroundFamily and school environment play a crucial role across the different developmental stages of adolescence. This paper investigates the potential mediating role of family and school environments in the relationship between the three psychosocial predictors of depression, anxiety, stress, and Internet addiction (IA). Specifically, it focuses on the two stages of early and middle adolescence.MethodsThe study involved a survey of 407 adolescents from Qatar, comprising 250 early adolescents and 157 middle adolescents. Inclusion criteria for the study included adolescents between the ages of 10 to 17 years old, residents of Qatar and studying in a Qatar-based school. To assess the constructs of the three psychosocial predictors, IA, family environment, the study utilized the Depression, Stress, and Anxiety Scale (DASS), the Internet Addiction Diagnostic Questionnaire (IADQ), and the Brief Family Relationship Scale, respectively. School environment was measured using questions from the “Health Behavior in School-aged Children: WHO Collaborative Cross-National survey/study (HBSC) 2013–2014. The study applied standard mediation analysis between the DASS components and IA with family and school environment as the mediators.ResultsResults from the mediation analysis reveal insights into the relationships between psychosocial predictors and IA. The findings indicate that family and school environments partially mediated the relationship with regards to depression, stress, and anxiety in early adolescents. In middle adolescents, family environment partially mediated the relationship with depression and stress and fully mediating the relationship with anxiety. Meanwhile, school environment only exhibited partial mediation in the relationship with anxiety in middle adolescence.ConclusionsThese results highlight the crucial role parents and schools play in addressing problematic technology usage that develops as a response to depression, anxiety, and stress among adolescents. Moreover, the study reveals nuances in the mediating role of family and school environment in early and middle adolescence. This highlights the evolving nature of these influences across the different stages of development. Notably, this study contributes to the literature by moving beyond the conventional focus on the so-called WEIRD population, and offering valuable insights from a region that is underrepresented in current research.

Estimation of Mediation Effect on Zero-Inflated Microbiome Mediators

The mediation analysis methodology of the cause-and-effect relationship through mediators has been increasingly popular over the past decades. The human microbiome can contribute to the pathogenesis of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate for microbiome data due to the excessive number of zero values and the over-dispersion in the sequencing reads, which arise for both biological and sampling reasons. To address these unique challenges brought by the zero-inflated mediator, we developed a novel mediation analysis algorithm under the potential-outcome framework to fill this gap. The proposed semiparametric model estimates the mediation effect of the microbiome by decomposing indirect effects into two components according to the zero-inflated distributions. The bootstrap algorithm is utilized to calculate the empirical confidence intervals of the causal effects. We conducted extensive simulation studies to investigate the performance of the proposed weighting-based approach and some model-based alternatives, and our proposed model showed robust performance. The proposed algorithm was implemented in a real human microbiome study of identifying whether some taxa mediate the relationship between LACTIN-V treatment and immune response.

Measuring Evidence for Mediation in the Presence of Measurement Error

Mediation analysis empirically investigates the process underlying the effect of an experimental manipulation on a dependent variable of interest. In the simplest mediation setting, the experimental treatment can affect the dependent variable through the mediator (indirect effect) and/or directly (direct effect). However, what appears to be an indirect effect in standard mediation analysis may reflect a data-generating process without mediation, including the possibility of a reversed causal ordering of measured variables, regardless of the statistical properties of the estimate. To overcome this indeterminacy where possible, the authors develop the insight that a statistically reliable total effect, combined with strong evidence for conditional independence of the treatment and the outcome given the mediator, is unequivocal evidence for mediation as the underlying causal model into an operational procedure. This is particularly helpful when theory is insufficient to definitely causally order measured variables, or when the dependent variable is measured before what is believed to be the mediator. The procedure combines Bayes factors as principled measures of the degree of support for conditional independence, with latent variable modeling to account for measurement error and discretization in a fully Bayesian framework. The authors reanalyze a set of published mediation studies to illustrate how their approach facilitates stronger conclusions.

PhyloMed: a phylogeny-based test of mediation effect in microbiome

Microbiome data from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator challenges the validity of standard mediation analyses. We propose a phylogeny-based mediation analysis method called PhyloMed to address this challenge. Unlike existing methods that directly identify individual mediating taxa, PhyloMed discovers mediation signals by analyzing subcompositions defined on the phylogenic tree. PhyloMed produces well-calibrated mediation test p-values and yields substantially higher discovery power than existing methods.

Glycated Hemoglobin is a Significant Predictor of Femoral, but Not of Carotid or Popliteal, Intima-Media Thickness in Adolescents with Type 1 Diabetes: A Case-Series Study

In healthy adults, the association between the glycated hemoglobin A1c (HbA1c) level and intima-media thickness (IMT) is stronger in the femoral artery than that in the carotid artery. However, whether this differential strength of association also applies to adolescents with type 1 diabetes (T1D) is unknown. Therefore, this study aimed to examine whether IMT increases in extracarotid arteries (specifically in the lower extremities) prior to the carotid artery. In total, 286 adolescents with T1D (15.9 ± 4.9 years; 42.0% male participants) were enrolled, and the B-mode ultrasonographic measurement of IMT in the carotid, femoral, and popliteal arteries was performed. Cardiovascular risk factors, including blood pressure (BP), body mass index, lipid levels, and glycemic parameters, were evaluated. To evaluate the site-dependent relationship between IMT and cardiovascular risk factors, a linear mixed-effects model was developed with repeated IMT measurements at various arterial sites as fixed effects and participants as random effects. Glycemic parameters, lipids, uric acid, high-sensitivity C-reactive protein, and advanced glycation end-products were some cardiovascular risk factors that worsened with increasing HbA1c levels. Patients with a higher HbA1c level (>10% vs. ≤10%) had thicker IMT in the femoral artery but not in the carotid or popliteal arteries. Patients with poorer diabetic control exhibited significant changes in certain cardiovascular functions, including central systolic BP, left ventricular (LV) ejection time, LV dp/dt max, stroke volume, and brachial artery compliance. A standard mediation analysis revealed that none of the aforementioned cardiovascular functions mediated the relationship between higher HbA1c level and greater femoral IMT. In adolescents with T1D, cardiovascular risk factors deteriorate with worsening blood glucose control. In the early stages of T1D, femoral IMT may serve as a more sensitive surrogate marker for hyperglycemia-induced subclinical atherosclerosis, an effect that may not be mediated by alterations in cardiovascular functions.

MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data.

Background: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). Methods: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. Results: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. Conclusions: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.

Personal, Social, and Environmental Mediators Associated With Increased Recreational Physical Activity in Women and Girls in the Kingdom of Tonga.

The purpose of this paper was to identify personal, social, and environmental mediators of recreational physical activity (PA) in a 6-month netball-based intervention for women and girls in Tonga. Tonga Netball's "low-engagement village program" was implemented in 10 villages and aimed to increase the recreational PA levels in women and girls through a comprehensive, structured community-level netball program addressing key barriers to participation. In a mixed-methods approach, these mediating barriers were identified through qualitative interviews based on the socioecological model. Quantitative measures for mediators and recreational PA were then developed, and data from 301 women and girls were collected. Standard mediation analyses methods were then applied. Program participation appeared to significantly increase PA levels. Statistically significant personal mediators were body issues, preferring competitions, and clothing. Social mediators were support from sports council, community leaders, friends, and church. Environmental mediators were travel time and access to balls, bibs, and umpires. A comprehensive community-level program addressing key participation barriers can increase recreational PA among women and girls in Tonga. Triangulating these results with mediation analyses of variables on the causal pathway can strengthen our understanding of causation and inform funding prioritization for critical program components in similar contexts.

Testing cell-type-specific mediation effects in genome-wide epigenetic studies.

Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, etal. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418-24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.

IFunMed: Integrative functional mediation analysis of GWAS and eQTL studies.

Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data is providing unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Although there have been many advances in incorporating prior information into prioritization of trait-associated variants in GWAS, functional annotation data have played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence for association. To address this, we develop a novel mediation framework, iFunMed, to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. Data-driven computational experiments convey how informative annotations improve single-nucleotide polymorphism (SNP) selection performance while emphasizing robustness of iFunMed to noninformative annotations. Application to Framingham Heart Study data indicates that iFunMed is able to boost detection of SNPs with mediation effects that can be attributed to regulatory mechanisms.

Compositional mediation analysis for microbiome studies

Motivated by recent advances in causal mediation analysis and problems in the analysis of microbiome data, we consider the setting where the effect of a treatment on an outcome is transmitted through perturbing the microbial communities or compositional mediators. The compositional and high-dimensional nature of such mediators makes the standard mediation analysis not directly applicable to our setting. We propose a sparse compositional mediation model that can be used to estimate the causal direct and indirect (or mediation) effects utilizing the algebra for compositional data in the simplex space. We also propose tests of total and component-wise mediation effects. We conduct extensive simulation studies to assess the performance of the proposed method and apply the method to a real microbiome dataset to investigate an effect of fat intake on body mass index mediated through the gut microbiome.

Vestibular roll tilt thresholds partially mediate age-related effects on balance.

It has been well-established that both vestibular function and balance degrade with age and that balance degradation contributes to falls. While multiple causes contribute to balance declines, there have been few empirical investigations of the specific sensory contributors to balance that mediate (i.e., explain a significant fraction of) the effect of age on balance. To determine if vestibular function significantly mediates the effect of age on balance, and to quantify the fraction of any such statistically significant age-effect on balance using previously published vestibular threshold and balance data. Balance was quantified as complete/incomplete on a standard Romberg 4-condition foam balance test. Vestibular thresholds were determined using standard methods with motion provided by a Moog 6DOF motion platform. Standard mediation analyses were performed to determine if any of the five vestibular thresholds measured (0.2Hz roll tilt and 1Hz roll tilt, yaw rotation, y-translation, and z-translation) significantly mediated the previously reported age-effect on balance. 0.2Hz roll tilt thresholds were found to significantly mediate the relationship between age and balance, whether we considered all subjects or just the subjects above the age of 40 (above which vestibular thresholds increase with age). Depending on the exact age cut-off implemented between 37 and 42 years of age, 0.2Hz roll tilt thresholds explained (mediated) between 33% and 55% of the total age-effect on balance. Vestibular function may mediate approximately 50% of the widely-reported age-effect on balance. If confirmed by future studies, this may provide an opportunity to improve balance (and presumably reduce fall risk) via specific therapies tailored to improve vestibular function.

Multiple mediation analysis for interval-valued data

Mediation analysis is an important statistical approach to evaluate the relationships among observed variables. The most commonly used models for mediation analysis handle single-valued variables. However, there are several circumstances (e.g., dimensionality reduction of large datasets, clinical patient courses, repeated measures, masked data, uncertain data) in which the collected information can be represented more naturally by means of intervals. In these cases, standard mediation analyses can be ill-suited. Although interval-valued variables can be transformed into standard single-valued variables, such procedures may mask some relevant information provided by intervals. In this article, we present a novel and simple model (IMedA) to perform mediation analysis on interval-valued variables which is based on both the symbolic regression approach and the regression based mediation framework. We also generalize Stolzenberg’s decomposition of effects to cope with interval-valued data. We further introduce a specific variance based decomposition procedure to descriptively evaluate the sizes of such effects. Finally, to better highlight the IMedA features we apply our model to a real case study from behavioral contexts.

A Multivariate Generalized Linear Model Approach to Mediation Analysis and Application of Confidence Ellipses

Mediation analysis evaluates the significance of an intermediate variable on the causal pathway between an exposure and an outcome. One commonly utilized test for mediation involves evaluation of counterfactual effects, estimated from separate regression models, corresponding to a composite null hypothesis. However, the “compositeness” of this null hypothesis is not commonly acknowledged and accounted for in mediation analyses. We describe a generalized multivariate approach in which these separate regression models are fit simultaneously in a single parsimonious model. This multivariate modeling approach can reproduce standard mediation analysis and has notable advantages over separate regression models, including the ability to combine distributions in the exponential family with any link functions and perform likelihood-based tests of some relevant hypotheses using existing software. We propose the use of a novel visual representation of confidence intervals of the two estimates for the indirect path with the use of a confidence ellipse. The calculation of the confidence ellipse is facilitated by the multivariate approach, can test the components of the composite null hypothesis under a single experiment-wise type I error rate, and does not require estimation of the standard error of the product of coefficients from two separate regressions. This method is illustrated using three examples. The first compares results between the multivariate method and separate regression models. The second example illustrates the proposed methods in the presence of an exposure–mediator interaction, missing data and confounding, and the third example utilizes these proposed methods for an outcome and mediator with negative binomial distributions.

Parametric Mediational g-Formula Approach to Mediation Analysis with Time-varying Exposures, Mediators, and Confounders

The assessment of direct and indirect effects with time-varying mediators and confounders is a common but challenging problem, and standard mediation analysis approaches are generally not applicable in this context. The mediational g-formula was recently proposed to address this problem, paired with a semiparametric estimation approach to evaluate longitudinal mediation effects empirically. In this article, we develop a parametric estimation approach to the mediational g-formula, including a feasible algorithm implemented in a freely available SAS macro. In the Framingham Heart Study data, we apply this method to estimate the interventional analogues of natural direct and indirect effects of smoking behaviors sustained over a 10-year period on blood pressure when considering weight change as a time-varying mediator. Compared with not smoking, smoking 20 cigarettes per day for 10 years was estimated to increase blood pressure by 1.2 mm Hg (95% CI: -0.7, 2.7). The direct effect was estimated to increase blood pressure by 1.5 mm Hg (95% CI: -0.3, 2.9), and the indirect effect was -0.3 mm Hg (95% CI: -0.5, -0.1), which is negative because smoking which is associated with lower weight is associated in turn with lower blood pressure. These results provide evidence that weight change in fact partially conceals the detrimental effects of cigarette smoking on blood pressure. Our study represents, to our knowledge, the first application of the parametric mediational g-formula in an epidemiologic cohort study (see video abstract at, http://links.lww.com/EDE/B159.).

Robust mediation analysis based on median regression.

Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers.

The Use of Propensity Scores in Mediation Analysis

Mediation analysis uses measures of hypothesized mediating variables to test theory for how a treatment achieves effects on outcomes and to improve subsequent treatments by identifying the most efficient treatment components. Most current mediation analysis methods rely on untested distributional and functional form assumptions for valid conclusions, especially regarding the relation between the mediator and outcome variables. Propensity score methods offer an alternative whereby the propensity score is used to compare individuals in the treatment and control groups who would have had the same value of the mediator had they been assigned to the same treatment condition. This article describes the use of propensity score weighting for mediation with a focus on explicating the underlying assumptions. Propensity scores have the potential to offer an alternative estimation procedure for mediation analysis with alternative assumptions from those of standard mediation analysis. The methods are illustrated investigating the mediational effects of an intervention to improve sense of mastery to reduce depression using data from the Job Search Intervention Study (JOBS II). We find significant treatment effects for those individuals who would have improved sense of mastery when in the treatment condition but no effects for those who would not have improved sense of mastery under treatment.

Odds Ratios for Mediation Analysis for a Dichotomous Outcome

For dichotomous outcomes, the authors discuss when the standard approaches to mediation analysis used in epidemiology and the social sciences are valid, and they provide alternative mediation analysis techniques when the standard approaches will not work. They extend definitions of controlled direct effects and natural direct and indirect effects from the risk difference scale to the odds ratio scale. A simple technique to estimate direct and indirect effect odds ratios by combining logistic and linear regressions is described that applies when the outcome is rare and the mediator continuous. Further discussion is given as to how this mediation analysis technique can be extended to settings in which data come from a case-control study design. For the standard mediation analysis techniques used in the epidemiologic and social science literatures to be valid, an assumption of no interaction between the effects of the exposure and the mediator on the outcome is needed. The approach presented here, however, will apply even when there are interactions between the effect of the exposure and the mediator on the outcome.

Sales Effects of Attention to Feature Advertisements: A Bayesian Mediation Analysis

There is much evidence that the presence of a feature advertisement can increase the sales and market share of the featured product. However, little is known about how feature ad characteristics (e.g., size, color, and location of the advertisement) affect the sales outcomes and how the effects take place. Prior research has predicted that feature advertisements lead to behavioral outcomes through their effect on consumers' attention. Building on this idea, the authors propose a Bayesian statistical model to study how feature ad characteristics affect sales of the featured products and the mediating role of attention in these relationships. They use data from eye-tracking tests of feature advertisements, aggregated and matched with sales data at the level of the feature advertisement. Their approach accounts for endogeneity in the key variables involved and overcomes limitations of standard mediation analyses. They show that the gaze duration on a feature advertisement affects sales of the featured product beyond the mere presence of the advertisement and that a standard mediation analysis that does not accommodate endogeneity produces biased estimates of the effects of feature ad characteristics on sales. Their proposed methodology is widely applicable to mediation analyses. The findings imply that attention data collected in lab tests can help marketers compare the relative sales outcomes of different feature ad designs and improve the effectiveness and efficiency of feature adverting decisions.

Mediation analysis with principal stratification

In assessing the mechanism of treatment efficacy in randomized clinical trials, investigators often perform mediation analyses by analyzing if the significant intent-to-treat treatment effect on outcome occurs through or around a third intermediate or mediating variable: indirect and direct effects, respectively. Standard mediation analyses assume sequential ignorability, i.e. conditional on covariates the intermediate or mediating factor is randomly assigned, as is the treatment in a randomized clinical trial. This research focuses on the application of the principal stratification (PS) approach for estimating the direct effect of a randomized treatment but without the standard sequential ignorability assumption. This approach is used to estimate the direct effect of treatment as a difference between expectations of potential outcomes within latent subgroups of participants for whom the intermediate variable behavior would be constant, regardless of the randomized treatment assignment. Using a Bayesian estimation procedure, we also assess the sensitivity of results based on the PS approach to heterogeneity of the variances among these principal strata. We assess this approach with simulations and apply it to two psychiatric examples. Both examples and the simulations indicated robustness of our findings to the homogeneous variance assumption. However, simulations showed that the magnitude of treatment effects derived under the PS approach were sensitive to model mis-specification.

Causal Mediation Analyses with Rank Preserving Models

We present a linear rank preserving model (RPM) approach for analyzing mediation of a randomized baseline intervention's effect on a univariate follow-up outcome. Unlike standard mediation analyses, our approach does not assume that the mediating factor is also randomly assigned to individuals in addition to the randomized baseline intervention (i.e., sequential ignorability), but does make several structural interaction assumptions that currently are untestable. The G-estimation procedure for the proposed RPM represents an extension of the work on direct effects of randomized intervention effects for survival outcomes by Robins and Greenland (1994, Journal of the American Statistical Association 89, 737-749) and on intervention non-adherence by Ten Have et al. (2004, Journal of the American Statistical Association 99, 8-16). Simulations show good estimation and confidence interval performance by the proposed RPM approach under unmeasured confounding relative to the standard mediation approach, but poor performance under departures from the structural interaction assumptions. The trade-off between these assumptions is evaluated in the context of two suicide/depression intervention studies.