scholarly journals Compositional Mediation Analysis for Microbiome Studies

2017 ◽  
Author(s):  
Michael B. Sohn ◽  
Hongzhe Li
Keyword(s):  
Mediation Analysis ◽  
Compositional Data ◽  
Fat Intake ◽  
Simulation Studies ◽  
Mediation Model ◽  
Mediation Effects ◽  
Microbiome Composition ◽  
Causal Mediation Analysis ◽  
Simplex Space ◽  
Microbiome Data

AbstractMotivated 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. 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 using bootstrap. We conduct extensive simulation studies to assess the performance of the proposed method and apply the method to a real metagenomic dataset to investigate the effect of fat intake on body mass index mediated through the gut microbiome composition.

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.

scholarly journals Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis

2021 ◽  
Author(s):  
Judith J. M. Rijnhart ◽  
Matthew J. Valente ◽  
Heather L. Smyth ◽  
David P. MacKinnon
Keyword(s):  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects ◽  
Mediation Model ◽  
Binary Outcome ◽  
Binary Variables ◽  
Causal Mediation ◽  
Causal Mediation Analysis ◽  
Similarities And Differences ◽  
Mediation Models

AbstractMediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure–mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables.

A compositional mediation model for a binary outcome: Application to microbiome studies

2021 ◽  
Author(s):  
Michael B Sohn ◽  
Jiarui Lu ◽  
Hongzhe Li
Keyword(s):  
R Package ◽  
External Factors ◽  
Supplementary Information ◽  
Binary Outcomes ◽  
Mediation Model ◽  
Probit Regression ◽  
Mediation Effects ◽  
Microbiome Research ◽  
Zero Sum ◽  
Microbiome Data

Abstract Motivation The delicate balance of the microbiome is implicated in our health and is shaped by external factors, such as diet and xenobiotics. Therefore, understanding the role of the microbiome in linking external factors and our health conditions is crucial to translate microbiome research into therapeutic and preventative applications. Results We introduced a sparse compositional mediation model for binary outcomes to estimate and test the mediation effects of the microbiome utilizing the compositional algebra defined in the simplex space and a linear zero-sum constraint on probit regression coefficients. For this model with the standard causal assumptions, we showed that both the causal direct and indirect effects are identifiable. We further developed a method for sensitivity analysis for the assumption of the no unmeasured confounding effects between the mediator and the outcome. We conducted extensive simulation studies to assess the performance of the proposed method and applied it to real microbiome data to study mediation effects of the microbiome on linking fat intake to overweight/obesity. Availability and implementation An R package can be downloaded from https://github.com/mbsohn/cmmb. Supplementary information Supplementary files 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 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.

scholarly journals Causal Mediation Analysis in the Presence of a Misclassified Binary Exposure

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhichao Jiang ◽  
Tyler VanderWeele
Keyword(s):  
Preterm Birth ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Smoking Status ◽  
Birth Certificate ◽  
Simulation Studies ◽  
Health Statistics ◽  
Intermediate Variable ◽  
Differential Misclassification ◽  
Causal Mediation Analysis

Abstract Mediation analysis is popular in examining the extent to which the effect of an exposure on an outcome is through an intermediate variable. When the exposure is subject to misclassification, the effects estimated can be severely biased. In this paper, when the mediator is binary, we first study the bias on traditional direct and indirect effect estimates in the presence of conditional non-differential misclassification of a binary exposure. We show that in the absence of interaction, the misclassification of the exposure will bias the direct effect towards the null but can bias the indirect effect in either direction. We then develop an EM algorithm approach to correcting for the misclassification, and conduct simulation studies to assess the performance of the correction approach. Finally, we apply the approach to National Center for Health Statistics birth certificate data to study the effect of smoking status on the preterm birth mediated through pre-eclampsia.

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 Estimation and Inference for the Mediation Effect in a Time-Varying Mediation Model

2021 ◽  
Author(s):  
Xizhen Cai ◽  
Donna Coffman ◽  
Megan Piper ◽  
Runze Li
Keyword(s):  
Indirect Effect ◽  
Mediation Analysis ◽  
Mediation Effect ◽  
Outcome Variable ◽  
Time Varying ◽  
Mediation Model ◽  
Real World Data ◽  
Mediation Effects ◽  
Simulation Based ◽  
Time Invariant

Abstract Background: Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e. the indirect effect through the mediator). This indirect effect is frequently assumed to be time-invariant. With improvements in data collection technology, it is possible to obtain repeated assessments over time resulting in intensive longitudinal data. This calls for an extension of traditional mediation analysis to incorporate time-varying variables as well as time-varying effects. Methods: We focus on estimation and inference for the time-varying mediation model, which allows mediation effects to vary as a function of time. We propose a two-step approach to estimate the time-varying mediation effect. Moreover, we use a simulation-based approach to derive the corresponding point-wise conffidence band for making inference of the time-varying mediation effect. Results: Simulation studies show that the proposed procedures perform well when comparing the conffidence band and the true underlying model. We further apply the proposed model and the statistical inference procedure to real-world data collected from a smoking cessation study. Conclusions: We present a practical model for estimating the time-varying mediation effects to allow time-varying outcome as well as time-varying mediator. Simulation-based inference tool is also proposed and implemented an R package on CRAN.

Sign in / Sign up

Export Citation Format

Share Document