scholarly journals Within-Subject Mediation Analysis in AB/BA Crossover Designs

2015 ◽  
Vol 11 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Haeike Josephy ◽  
Stijn Vansteelandt ◽  
Marie-Anne Vanderhasselt ◽  
Tom Loeys
Keyword(s):  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects ◽  
Crossover Designs ◽  
Intermediate Variable ◽  
Crossover Trials ◽  
Scant Attention ◽  
Underlying Mechanisms ◽  
Insight Into

AbstractCrossover trials are widely used to assess the effect of a reversible exposure on an outcome of interest. To gain further insight into the underlying mechanisms of this effect, researchers may be interested in exploring whether or not it runs through a specific intermediate variable: the mediator. Mediation analysis in crossover designs has received scant attention so far and is mostly confined to the traditional Baron and Kenny approach. We aim to tackle mediation analysis within the counterfactual framework and elucidate the assumptions under which the direct and indirect effects can be identified in

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.

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

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 Mediation analysis for count and zero-inflated count data

2017 ◽  
Vol 27 (9) ◽  
pp. 2756-2774 ◽  
Author(s):  
Jing Cheng ◽  
Nancy F Cheng ◽  
Zijian Guo ◽  
Steven Gregorich ◽  
Amid I Ismail ◽  
...  
Keyword(s):  
Social Sciences ◽  
Motivational Interviewing ◽  
Mediation Analysis ◽  
Count Data ◽  
Indirect Effects ◽  
Nonlinear Models ◽  
Dental Health ◽  
Simulation Studies ◽  
Direct And Indirect Effects ◽  
Outcome Framework

Different conventional and causal approaches have been proposed for mediation analysis to better understand the mechanism of a treatment. Count and zero-inflated count data occur in biomedicine, economics, and social sciences. This paper considers mediation analysis for count and zero-inflated count data under the potential outcome framework with nonlinear models. When there are post-treatment confounders which are independent of, or affected by, the treatment, we first define the direct, indirect, and total effects of our interest and then discuss various conditions under which the effects of interest can be identified. Proofs are provided for the sensitivity analysis proposed in the paper. Simulation studies show that the methods work well. We apply the methods to the Detroit Dental Health Project’s Motivational Interviewing DVD trial for the direct and indirect effects of motivational interviewing on count and zero-inflated count dental caries outcomes.

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