scholarly journals Conducting Regression-based Causal Mediation Analysis Using the R Package "regmedint"

2020 ◽  
Author(s):  
Kazuki Yoshida ◽  
Maya B Mathur ◽  
Robert J. Glynn
Keyword(s):  
Mediation Analysis ◽  
R Package ◽  
Causal Mediation ◽  
Regression Formula ◽  
Causal Mediation Analysis

The R package regmedint is a complete implementation of the regression formula-based causal mediation analysis.

scholarly journals Generalized causal mediation and path analysis: Extensions and practical considerations

2018 ◽  
Vol 28 (6) ◽  
pp. 1793-1807 ◽  
Author(s):  
Jeffrey M Albert ◽  
Jang Ik Cho ◽  
Yiying Liu ◽  
Suchitra Nelson
Keyword(s):  
Path Analysis ◽  
Mediation Analysis ◽  
Linear Models ◽  
Reference Group ◽  
Specific Effect ◽  
R Package ◽  
Companion Paper ◽  
New Method ◽  
Causal Mediation ◽  
Causal Mediation Analysis

Causal mediation analysis seeks to decompose the effect of a treatment or exposure among multiple possible paths and provide casually interpretable path-specific effect estimates. Recent advances have extended causal mediation analysis to situations with a sequence of mediators or multiple contemporaneous mediators. However, available methods still have limitations, and computational and other challenges remain. The present paper provides an extended causal mediation and path analysis methodology. The new method, implemented in the new R package, gmediation (described in a companion paper), accommodates both a sequence (two stages) of mediators and multiple mediators at each stage, and allows for multiple types of outcomes following generalized linear models. The methodology can also handle unsaturated models and clustered data. Addressing other practical issues, we provide new guidelines for the choice of a decomposition, and for the choice of a reference group multiplier for the reduction of Monte Carlo error in mediation formula computations. The new method is applied to data from a cohort study to illuminate the contribution of alternative biological and behavioral paths in the effect of socioeconomic status on dental caries in adolescence.

scholarly journals R package regmedint: extension of regression-based causal mediation analysis with effect measure modification by covariates

2022 ◽  
Author(s):  
Yi Li ◽  
Maya B Mathur ◽  
Kazuki Yoshida
Keyword(s):  
Mediation Analysis ◽  
Regression Models ◽  
R Package ◽  
Causal Mediation ◽  
Effect Measure ◽  
Supplementary Document ◽  
Sas Macro ◽  
Causal Mediation Analysis ◽  
Effect Measure Modification

This is the supplementary document of R package regmedint that implements the extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013, 2015). It supports including effect measure modification by covariates (treatment-covariate and mediator-covariate product terms in mediator and outcome regression models), and also accommodates the original SAS macro and PROC CAUSALMED procedure in SAS when there is no effect measure modification.

P067 Mechanisms for the effect of CFRD on mortality: a causal mediation analysis using UK cystic fibrosis Registry data

2021 ◽  
Vol 20 ◽  
pp. S59
Author(s):  
K.T. Tanner ◽  
D. Bilton ◽  
R.M. Daniel ◽  
R.H. Keogh ◽  
L.D. Sharples
Keyword(s):  
Cystic Fibrosis ◽  
Mediation Analysis ◽  
Registry Data ◽  
Causal Mediation ◽  
Causal Mediation Analysis

scholarly journals Exact parametric causal mediation analysis for a binary outcome with a binary mediator

2021 ◽  
Author(s):  
Marco Doretti ◽  
Martina Raggi ◽  
Elena Stanghellini
Keyword(s):  
Logistic Regression ◽  
Direct Effect ◽  
Mediation Analysis ◽  
Delta Method ◽  
Binary Outcome ◽  
Effect Decomposition ◽  
Causal Mediation ◽  
Regression Parameters ◽  
Causal Mediation Analysis ◽  
Parametric Expression

AbstractWith reference to causal mediation analysis, a parametric expression for natural direct and indirect effects is derived for the setting of a binary outcome with a binary mediator, both modelled via a logistic regression. The proposed effect decomposition operates on the odds ratio scale and does not require the outcome to be rare. It generalizes the existing ones, allowing for interactions between both the exposure and the mediator and the confounding covariates. The derived parametric formulae are flexible, in that they readily adapt to the two different natural effect decompositions defined in the mediation literature. In parallel with results derived under the rare outcome assumption, they also outline the relationship between the causal effects and the correspondent pathway-specific logistic regression parameters, isolating the controlled direct effect in the natural direct effect expressions. Formulae for standard errors, obtained via the delta method, are also given. An empirical application to data coming from a microfinance experiment performed in Bosnia and Herzegovina is illustrated.

Factors explaining socio-economic inequalities in survival from colon cancer: a causal mediation analysis

2021 ◽  
pp. cebp.0222.2021
Author(s):  
Nina Afshar ◽  
S. Ghazaleh Dashti ◽  
Luc te Marvelde ◽  
Tony Blakely ◽  
Andrew Haydon ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Mediation Analysis ◽  
Causal Mediation ◽  
Causal Mediation Analysis ◽  
Economic Inequalities

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.

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.

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