scholarly journals The Failings of Conventional Mediation Analysis and a Design-Based Alternative

2021 ◽  
Vol 4 (4) ◽  
pp. 251524592110472
Author(s):  
John G. Bullock ◽  
Donald P. Green
Keyword(s):  
Experimental Design ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects ◽  
Personality Psychology ◽  
Statistical Procedures ◽  
Experimental Approaches

Scholars routinely test mediation claims by using some form of measurement-of-mediation analysis whereby outcomes are regressed on treatments and mediators to assess direct and indirect effects. Indeed, it is rare for an issue of any leading journal of social or personality psychology not to include such an analysis. Statisticians have for decades criticized this method on the grounds that it relies on implausible assumptions, but these criticisms have been largely ignored. After presenting examples and simulations that dramatize the weaknesses of the measurement-of-mediation approach, we suggest that scholars instead use an approach that is rooted in experimental design. We propose implicit-mediation analysis, which adds and subtracts features of the treatment in ways that implicate some mediators and not others. We illustrate the approach with examples from recently published articles, explain the differences between the approach and other experimental approaches to mediation, and formalize the assumptions and statistical procedures that allow researchers to learn from experiments that encourage changes in mediators.

Direct and Indirect Effects of Crisaborole Ointment on Quality of Life in Patients with Atopic Dermatitis: A Mediation Analysis

2017 ◽  
Vol 1 ◽  
pp. s36
Author(s):  
Eric Simpson ◽  
Andrew Bushmakin ◽  
Joseph C Cappelleri ◽  
Thomas Luger ◽  
Sonja Stander ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Atopic Dermatitis ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects

Abstract Not Available

Bayesian sensitivity analysis for unmeasured confounding in causal mediation analysis

2017 ◽  
Vol 28 (2) ◽  
pp. 515-531 ◽  
Author(s):  
Lawrence C McCandless ◽  
Julian M Somers
Keyword(s):  
Sensitivity Analysis ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects ◽  
Unmeasured Confounding ◽  
Intermediate Variable ◽  
Causal Mediation ◽  
Important Concern ◽  
Analysis Technique ◽  
Causal Mediation Analysis

Causal mediation analysis techniques enable investigators to examine whether the effect of the exposure on an outcome is mediated by some intermediate variable. Motivated by a data example from epidemiology, we consider estimation of natural direct and indirect effects on a survival outcome. An important concern is bias from confounders that may be unmeasured. Estimating natural direct and indirect effects requires an elaborate series of assumptions in order to identify the target quantities. The analyst must carefully measure and adjust for important predictors of the exposure, mediator and outcome. Omitting important confounders may bias the results in a way that is difficult to predict. In recent years, several methods have been proposed to explore sensitivity to unmeasured confounding in mediation analysis. However, many of these methods limit complexity by relying on a handful of sensitivity parameters that are difficult to interpret, or alternatively, by assuming that specific patterns of unmeasured confounding are absent. Instead, we propose a simple Bayesian sensitivity analysis technique that is indexed by four bias parameters. Our method has the unique advantage that it is able to simultaneously assess unmeasured confounding in the mediator–outcome, exposure–outcome and exposure–mediator relationships. It is a natural Bayesian extension of the sensitivity analysis methodologies of VanderWeele, which have been widely used in the epidemiology literature. We present simulation findings, and additionally, we illustrate the method in an epidemiological study of mortality rates in criminal offenders from British Columbia.

Causal Mediation Analysis for Estimating Direct and Indirect Effects of an Environmental Mixture on Child Neurodevelopment

2018 ◽  
Vol 2017 (1) ◽  
pp. 433
Author(s):  
Katrina L. Devick ◽  
Jennifer F. Bobb ◽  
Maitreyi Mazumdar ◽  
Birgit Claus Henn ◽  
David C. Bellinger ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects ◽  
Causal Mediation ◽  
Child Neurodevelopment ◽  
Causal Mediation Analysis

scholarly journals Direct and Indirect Effects of Crisaborole Ointment on Quality of Life in Patients with Atopic Dermatitis: A Mediation Analysis

2019 ◽  
Vol 99 (9) ◽  
pp. 756-761
Author(s):  
E Simpson ◽  
G Yosipovitch ◽  
A Bushmakin ◽  
J Cappelleri ◽  
T Luger ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Atopic Dermatitis ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects

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.

scholarly journals Correlations and path analysis of yield components in cowpea

2014 ◽  
Vol 14 (2) ◽  
pp. 82-87 ◽  
Author(s):  
Adriano dos Santos ◽  
Gessí Ceccon ◽  
Livia Maria Chamma Davide ◽  
Agenor Martinho Correa ◽  
Valdecir Batista Alves
Keyword(s):  
Experimental Design ◽  
Grain Yield ◽  
Vigna Unguiculata ◽  
Indirect Effects ◽  
Yield Components ◽  
Indirect Selection ◽  
Direct And Indirect Effects ◽  
Genotypic And Phenotypic Correlations ◽  
Selection Of ◽  
Multiple Character

Obtaining correlations and direct and indirect effects of yield components is important for the selection of promising parental and segregating populations. The objective of this research was to estimate the genotypic and phenotypic correlations, and to analyze the direct and indirect effects of yield components on grain yield in 20 cowpea (Vigna unguiculata) genotypes. The experimental design was in randomized blocks with 20 treatments and 4 replications. The character showed low to moderate possibility of gain from indirect selection, with greater possibility for success when joining multiple character and a genotype of better performance.

Subduing attitude polarization?

2018 ◽  
Vol 37 (1) ◽  
pp. 68-77
Author(s):  
Eulàlia P. Abril
Keyword(s):  
Experimental Design ◽  
Indirect Effect ◽  
Indirect Effects ◽  
Assisted Suicide ◽  
Media Exposure ◽  
Online News ◽  
Direct And Indirect Effects ◽  
Attitude Polarization ◽  
Hostile Media ◽  
Partisan News

Researchers have sought to understand the effects of like-minded versus contrary news exposure on attitude polarization, which can be a threat to democracy. The online news environment offers opportunities for exposure tobothtypes of news, albeit unequally. This study tests the effects of exposure to heterogeneous partisan news bundles (both like-minded and contrary news) on attitude polarization. Because media exposure can lead to bias, attitude polarization is tested as a directandindirect effect via hostile media perceptions. Data in this study are from a between-subjects experimental design about the issue of assisted suicide. Results indicate that even though the effect of the partisan news bundle on hostile media perceptions is significant, both direct and indirect effects on attitude polarization are null.

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