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 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 Causal Mediation Analysis

2011 ◽  
Vol 11 (4) ◽  
pp. 605-619 ◽  
Author(s):  
Raymond Hicks ◽  
Dustin Tingley
Keyword(s):  
Mediation Analysis ◽  
Parametric Models ◽  
Sensitivity Analyses ◽  
Statistical Methodology ◽  
Mediation Effects ◽  
Explanatory Variables ◽  
Causal Mediation ◽  
Different Types ◽  
Recent Developments ◽  
Causal Mediation Analysis

Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key identifying assumption required for interpreting mediation results causally.

scholarly journals Bayesian causal mediation analysis with multiple ordered mediators

2018 ◽  
Vol 19 (6) ◽  
pp. 634-652
Author(s):  
Tianming Gao ◽  
Jeffrey M Albert
Keyword(s):  
Mediation Analysis ◽  
Mixed Model ◽  
Dental Health ◽  
Health Study ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Causal Pathways ◽  
Sensitivity Parameter ◽  
Causal Pathway ◽  
Causal Mediation Analysis

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.

High-dimensional Causal Mediation Analysis with a Large Number of Mediators Clumping at Zero to Assess the Contribution of the Microbiome to the Risk of Bacterial Pathogen Colonization in Older Adults

2020 ◽  
Vol 15 (7) ◽  
pp. 671-696
Author(s):  
Wei Liu ◽  
John P. Haran ◽  
Arlene S. Ash ◽  
Jeroan J. Allison ◽  
Shangyuan Ye ◽  
...  
Keyword(s):  
Older Adults ◽  
Mediation Analysis ◽  
Bacterial Pathogen ◽  
Outcome Variable ◽  
High Dimensional ◽  
Modeling Framework ◽  
Causal Mediation ◽  
Causal Pathways ◽  
Causal Mediation Analysis ◽  
Pathogen Colonization

Background: Causal mediation analysis is conducted in biomedical research with the goal of investigating causal mechanisms that consist of both direct causal pathways between the treatment and outcome variables and intermediate causal pathways through mediators. Recently, this type of analysis has been applied in the context of bioinformatics; however, it encounters the obstacle of high-dimensional and semi-continuous mediators with clumping at zero. Methods: In this article, we develop a methodology to conduct high-dimensional causal mediation analysis with a modeling framework that involves (i) a nonlinear model for the outcome variable, (ii) two-part models for semi-continuous mediators with clumping at zero, and (iii) sophisticated variable-selection techniques using machine learning. We conducted simulations and investigated the performance of the proposed method. It is shown that the proposed method can provide reliable statistical information on the causal effects with high-dimensional mediators. The method is adopted to assess the contribution of the intestinal microbiome to the risk of bacterial pathogen colonization in older adults from US nursing homes. Conclusions: The proposed high-dimensional causal mediation analysis with nonlinear models is an innovative and reliable approach to conduct causal inference with high-dimensional mediators.

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 A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance

2020 ◽  
Vol 8 (1) ◽  
pp. 131-149
Author(s):  
Soojin Park ◽  
Esra Kürüm
Keyword(s):  
Mediation Analysis ◽  
Joint Modeling ◽  
Modeling Method ◽  
Sensitivity Analyses ◽  
Two Stage ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Causal Mediation Analysis ◽  
Treatment Noncompliance ◽  
The Impact

AbstractEstimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation effects in the presence of treatment noncompliance. Existing studies based on the IV-based method focus on identifying the mediated portion of the intention-to-treat effect, which relies on several identification assumptions. However, little attention has been given to assessing the sensitivity of the identification assumptions or mitigating the impact of violating these assumptions. This study proposes a two-stage joint modeling method for conducting causal mediation analysis in the presence of treatment noncompliance, in which modeling assumptions can be employed to decrease the sensitivity to violation of some identification assumptions. The use of a joint modeling method is also conducive to conducting sensitivity analyses to the violation of identification assumptions. We demonstrate our approach using the Jobs II data, in which the effect of job training on job seekers’ mental health is examined.

Causal mediation analysis in instrumental-variables regressions

2020 ◽  
Vol 20 (3) ◽  
pp. 613-626
Author(s):  
Christian Dippel ◽  
Andreas Ferrara ◽  
Stephan Heblich
Keyword(s):  
Instrumental Variables ◽  
Treatment Effect ◽  
Mediation Analysis ◽  
Unpublished Manuscript ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Causal Treatment ◽  
Causal Mediation Analysis ◽  
Single Instrument ◽  
Mediator Variable

In this article, we describe the use of ivmediate, a new command to estimate causal mediation effects in instrumental-variables settings using the framework developed by Dippel et al. (2020, unpublished manuscript). ivmediate allows estimation of a treatment effect and the share of this effect that can be attributed to a mediator variable. While both treatment and mediator can be potentially endogenous, a single instrument suffices to identify both the causal treatment and the mediation effects.

scholarly journals Causal Mediation Analysis with Multiple Causally Ordered and Non-ordered Mediators based on Summarized Genetic Data

2021 ◽  
Author(s):  
Lei Hou ◽  
Yuanyuan Yu ◽  
Xiaoru Sun ◽  
Xinhui Liu ◽  
Yifan Yu ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Genetic Data ◽  
Type I ◽  
Finite Sample ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Causal Mediation Analysis ◽  
The Impact

Causal mediation analysis aims to investigate the mechanism linking an exposure and an outcome. Dealing with the impact of unobserved confounders among the exposure, mediator and outcome has always been an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, making it more difficult to estimate of path-specific effects (PSEs). In this article, we propose a method (PSE-MR) to identify and estimate PSEs of an exposure on an outcome through multiple causally ordered and non-ordered mediators using Mendelian Randomization, when there are unmeasured confounders among the exposure, mediators and outcome. Additionally, PSE-MR can be used when pleiotropy exists, and can be implemented using only summarized genetic data. We also conducted simulations to evaluate the finite sample performances of our proposed estimators in different scenarios. The results show that the causal estimates of PSEs are almost unbiased with good coverage and Type I error properties. We illustrate the utility of our method through a study of exploring the mediation effects of lipids in the causal pathways from body mass index to cardiovascular disease.

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