scholarly journals Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

Biometrics ◽  
2019 ◽  
Vol 76 (3) ◽  
pp. 700-710 ◽  
Author(s):  
Yanyi Song ◽  
Xiang Zhou ◽  
Min Zhang ◽  
Wei Zhao ◽  
Yongmei Liu ◽  
...  
Keyword(s):  
High Dimensional ◽  
Shrinkage Estimation ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Bayesian Shrinkage

scholarly journals Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies

2018 ◽  
Author(s):  
Yanyi Song ◽  
Xiang Zhou ◽  
Min Zhang ◽  
Wei Zhao ◽  
Yongmei Liu ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Epidemiological Data ◽  
Joint Analysis ◽  
High Dimensional ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Bayesian Inference Method ◽  
Bayesian Shrinkage ◽  
Causal Mediation Analysis ◽  
Shrinkage Priors

AbstractCausal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high-throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of omics data, joint analysis of molecular-level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high-dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true non-null mediators. We also construct tests for natural indirect effects using a permutation procedure. The Bayesian method helps us to understand the structure of the composite null hypotheses. We applied our method to Multi-Ethnic Study of Atherosclerosis (MESA) and identified DNA methylation regions that may actively mediate the effect of socioeconomic status (SES) on cardiometabolic outcome.

scholarly journals Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J Blaser ◽  
Huilin Li
Keyword(s):  
Regression Model ◽  
Association Studies ◽  
Statistical Significance ◽  
Mediation Effect ◽  
Supplementary Information ◽  
High Dimensional ◽  
Model Framework ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Microbiome Data

Abstract Motivation Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. Results We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. Availability and implementation https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J. Blaser ◽  
Huilin Li
Keyword(s):  
Regression Model ◽  
Association Studies ◽  
Statistical Significance ◽  
Mediation Effect ◽  
High Dimensional ◽  
Model Framework ◽  
Mediation Effects ◽  
Microbiome Composition ◽  
Causal Mediation ◽  
Microbiome Data

AbstractMotivationRecent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.ResultsWe propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight.

scholarly journals Estimating and testing high-dimensional mediation effects in epigenetic studies

2016 ◽  
Vol 32 (20) ◽  
pp. 3150-3154 ◽  
Author(s):  
Haixiang Zhang ◽  
Yinan Zheng ◽  
Zhou Zhang ◽  
Tao Gao ◽  
Brian Joyce ◽  
...  
Keyword(s):  
High Dimensional ◽  
Mediation Effects

Causal mediation in developmental science: A primer

2021 ◽  
pp. 016502542098164
Author(s):  
Jorge Cuartas ◽  
Dana Charles McCoy
Keyword(s):  
Best Practices ◽  
Mediation Analysis ◽  
Critical Role ◽  
Experimental Studies ◽  
Developmental Theory ◽  
Potential Outcomes ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Developmental Science ◽  
Potential Outcomes Framework

Mediation has played a critical role in developmental theory and research. Yet, developmentalists rarely discuss the methodological challenges of establishing causality in mediation analysis or potential strategies to improve the identification of causal mediation effects. In this article, we discuss the potential outcomes framework from statistics as a means for highlighting several fundamental challenges of establishing causality in mediation analysis, including the difficulty of meeting the key assumption of sequential ignorability, even in experimental studies. We argue that this framework—which, although commonplace in other fields, has not yet been taken up in developmental science—can inform solutions to these challenges. Based on the framework, we offer a series of recommendations for improving causal inference in mediation analysis, including an overview of best practices in both study design and analysis, as well as resources for conducting analysis. In doing so, our overall objective in this article is to support the use of rigorous methods for understanding questions of mechanism in developmental science.

scholarly journals Causal mediation analysis with multiple causally non-ordered mediators

2015 ◽  
Vol 27 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Masataka Taguri ◽  
John Featherstone ◽  
Jing Cheng
Keyword(s):  
Indirect Effect ◽  
Interactive Effect ◽  
Mediation Effect ◽  
Mediation Effects ◽  
Mediation Analyses ◽  
Intermediate Variable ◽  
Causal Mediation ◽  
Health Studies ◽  
Using Data ◽  
The Individual

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.

scholarly journals Prying Open the Black Box of Causality - A Causal Mediation Analysis Test of Procedural Justice Policing

2017 ◽  
Author(s):  
Krisztián Pósch
Keyword(s):  
Procedural Justice ◽  
Treatment Effect ◽  
Mediation Analysis ◽  
Black Box ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Treatment Implementation ◽  
Causal Mediation Analysis ◽  
The Impact ◽  
Sense Of Power

Objectives: Review causal mediation analysis as a method for estimating and assessing direct and indirect effects in experimental criminology. Test procedural justice theory by examining the extent to which procedural justice mediates the impact of contact with the police on various outcomes. Apply causal mediation analysis to better interpret data from a field experiment that had suffered from a particular type of implementation failure.Methods: Data from a block-randomised controlled trial of procedural justice policing (the Scottish Community Engagement Trial) were analysed. All constructs were measured using surveys distributed during roadside police checks. The treatment implementation was assessed by analysing the treatment effect consistency and heterogeneity. Causal mediation analysis and sensitivity analysis were used to assess the mediating role of procedural justice.Results: First, the treatment effect was consistent and fairly homogeneous, indicating that the systematic variation in the study is attributable to the design. Second, procedural justice acts as a mediator channelling the treatment’s effect towards normative alignment (NIE=-0.207), duty to obey (NIE=-0.153), sense of power (NIE=-0.078), and social identity (NIE=-0.052), all of which are moderately robust to unmeasured confounding. The NIEs for risk of sanction and personal morality were highly sensitive, while for coerced obligation and sense of power they were non-significant. Conclusions: Causal mediation analysis is a versatile tool that can salvage experiments with systematic yet ambiguous treatment effects by allowing researchers to “pry open” the black box of causality. Most of the theoretical propositions of procedural justice policing were supported. Future studies are needed with more discernible causal mediation effects.

scholarly journals PENGARUH KELEKATAN TERHADAP KESEJAHTERAAN PSIKOLOGIS YANG DIMEDIASI OLEH RESILIENSI PADA REMAJA DENGAN ORANG TUA BERCERAI

2020 ◽  
Vol 4 (1) ◽  
pp. 32-44
Author(s):  
Putri Puspitasari ◽  
Sri Maslihah ◽  
Anastasia Wulandari
Keyword(s):  
High School ◽  
Well Being ◽  
Peer Attachment ◽  
Psychological Well Being ◽  
Mediation Effects ◽  
Mediation Analyses ◽  
Causal Mediation ◽  
Other Hand ◽  
Resilience Scale

The aim of this study was to examine the correlation of attachment on psychological well-being with resilience act as mediator. Participants were 127 high school adolescents with parent divorced while aged 0 to 12 years old. Attachment was assessed by Inventory of Parent and Peer Attachment (IPPA), resilience was assessed by Resilience Scale (RS), and psychological well-being was assessed by Psychological Well-Being Scale (PWBS) instruments. Causal mediation analyses were used to examine the proposed mediation effects. Result showed that attachment can predict adolescents’ psychological well-being. On other hand, resilience functioned as a mediator on the correlation.

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