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.

scholarly journals Sensitivity analysis for unobserved confounding in causal mediation analysis allowing for effect modification, censoring and truncation

2021 ◽  
Author(s):  
Anita Lindmark
Keyword(s):  
Sensitivity Analysis ◽  
Mediation Analysis ◽  
Regression Models ◽  
Effect Modification ◽  
Outcome Variable ◽  
Parametric Regression ◽  
Intermediate Variable ◽  
Causal Mediation ◽  
Causal Mediation Analysis ◽  
Parametric Regression Models

AbstractCausal mediation analysis is used to decompose the total effect of an exposure on an outcome into an indirect effect, taking the path through an intermediate variable, and a direct effect. To estimate these effects, strong assumptions are made about unconfoundedness of the relationships between the exposure, mediator and outcome. These assumptions are difficult to verify in a given situation and therefore a mediation analysis should be complemented with a sensitivity analysis to assess the possible impact of violations. In this paper we present a method for sensitivity analysis to not only unobserved mediator-outcome confounding, which has largely been the focus of previous literature, but also unobserved confounding involving the exposure. The setting is estimation of natural direct and indirect effects based on parametric regression models. We present results for combinations of binary and continuous mediators and outcomes and extend the sensitivity analysis for mediator-outcome confounding to cases where the continuous outcome variable is censored or truncated. The proposed methods perform well also in the presence of interactions between the exposure, mediator and observed confounders, allowing for modeling flexibility as well as exploration of effect modification. The performance of the method is illustrated through simulations and an empirical example.

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.

SAS Macro for Causal Mediation Analysis with Survival Data

Epidemiology ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. e23-e24 ◽  
Author(s):  
Linda Valeri ◽  
Tyler J. VanderWeele
Keyword(s):  
Mediation Analysis ◽  
Survival Data ◽  
Causal Mediation ◽  
Sas Macro ◽  
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 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
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