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.

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.

High-dimensional Causal Mediation Analysis with a Large Number of Mediators Clumping at Zero to Assess the Contribution of the Microbiome to the Risk of Bacterial Pathogen Colonization in Older Adults

2020 ◽  
Vol 15 (7) ◽  
pp. 671-696
Author(s):  
Wei Liu ◽  
John P. Haran ◽  
Arlene S. Ash ◽  
Jeroan J. Allison ◽  
Shangyuan Ye ◽  
...  
Keyword(s):  
Older Adults ◽  
Mediation Analysis ◽  
Bacterial Pathogen ◽  
Outcome Variable ◽  
High Dimensional ◽  
Modeling Framework ◽  
Causal Mediation ◽  
Causal Pathways ◽  
Causal Mediation Analysis ◽  
Pathogen Colonization

Background: Causal mediation analysis is conducted in biomedical research with the goal of investigating causal mechanisms that consist of both direct causal pathways between the treatment and outcome variables and intermediate causal pathways through mediators. Recently, this type of analysis has been applied in the context of bioinformatics; however, it encounters the obstacle of high-dimensional and semi-continuous mediators with clumping at zero. Methods: In this article, we develop a methodology to conduct high-dimensional causal mediation analysis with a modeling framework that involves (i) a nonlinear model for the outcome variable, (ii) two-part models for semi-continuous mediators with clumping at zero, and (iii) sophisticated variable-selection techniques using machine learning. We conducted simulations and investigated the performance of the proposed method. It is shown that the proposed method can provide reliable statistical information on the causal effects with high-dimensional mediators. The method is adopted to assess the contribution of the intestinal microbiome to the risk of bacterial pathogen colonization in older adults from US nursing homes. Conclusions: The proposed high-dimensional causal mediation analysis with nonlinear models is an innovative and reliable approach to conduct causal inference with high-dimensional mediators.

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 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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