Point: Risk Ratio Equations for Natural Direct and Indirect Effects in Causal Mediation Analysis of a Binary Mediator and a Binary Outcome—A Fresh Look at the Formulas

2018 ◽  
Vol 188 (7) ◽  
pp. 1201-1203 ◽  
Author(s):  
Mariia Samoilenko ◽  
Geneviève Lefebvre
Keyword(s):  
Risk Ratio ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Binomial Model ◽  
Direct And Indirect Effects ◽  
Binary Outcome ◽  
Causal Mediation ◽  
Causal Mediation Analysis ◽  
Mediator Variable ◽  
Risk Ratios

Abstract In this article, we review the formulas for the natural direct and indirect effects’ risk ratios introduced by Ananth and VanderWeele (Am J Epidemiol. 2011;174(1):99–108) for causal mediation analysis of a binary mediator and a binary outcome. In particular, we show that the closed-form equations Ananth and VanderWeele provided do not correspond to the log-binomial model specified by these authors for the mediator variable, but rather to a logistic model. We then provide risk ratio equations for natural direct and indirect effects that truly pertain to a log-binomial model. We conclude with a discussion on the practical implications of the binary mediator model’s specification by analysts. The related impact can be negligible or not, depending on the rareness of the mediator.

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.

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 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.

Causal mediation analysis in instrumental-variables regressions

2020 ◽  
Vol 20 (3) ◽  
pp. 613-626
Author(s):  
Christian Dippel ◽  
Andreas Ferrara ◽  
Stephan Heblich
Keyword(s):  
Instrumental Variables ◽  
Treatment Effect ◽  
Mediation Analysis ◽  
Unpublished Manuscript ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Causal Treatment ◽  
Causal Mediation Analysis ◽  
Single Instrument ◽  
Mediator Variable

In this article, we describe the use of ivmediate, a new command to estimate causal mediation effects in instrumental-variables settings using the framework developed by Dippel et al. (2020, unpublished manuscript). ivmediate allows estimation of a treatment effect and the share of this effect that can be attributed to a mediator variable. While both treatment and mediator can be potentially endogenous, a single instrument suffices to identify both the causal treatment and the mediation effects.

A regression-with-residuals method for analyzing causal mediation: The rwrmed package

2021 ◽  
Vol 21 (3) ◽  
pp. 559-574
Author(s):  
Ariel Linden ◽  
Chuck Huber ◽  
Geoffrey T. Wodtke
Keyword(s):  
Treatment Outcome ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Direct And Indirect Effects ◽  
Conditional Mean ◽  
Causal Mediation ◽  
Unobserved Variables

In this article, we introduce the rwrmed package, which performs mediation analysis using the methods proposed by Wodtke and Zhou (2020, Epidemiology 31: 369–375). Specifically, rwrmed estimates interventional direct and indirect effects in the presence of treatment-induced confounding by fitting models for 1) the conditional mean of the mediator given the treatment and a set of baseline confounders and 2) the conditional mean of the outcome given the treatment, mediator, baseline confounders, and a set of treatment-induced confounders that have been residualized with respect to the observed past. Interventional direct and indirect effects are simple functions of the parameters in these models when they are correctly specified and when there are no unobserved variables that confound the treatment-outcome, treatment-mediator, or mediator-outcome relationships. When no treatment-induced confounders are specified, rwrmed produces natural direct and indirect effect estimates.

scholarly journals 1443A causal mediation analysis of the effect of disability on mental health mediated by employment

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Zoe Aitken ◽  
Julie Simpson ◽  
Rebecca Bentley ◽  
Anne Kavanagh
Keyword(s):  
Mental Health ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Causal Effect ◽  
People With Disabilities ◽  
Causal Mediation ◽  
Novel Approach ◽  
Causal Mediation Analysis ◽  
Employment Rates ◽  
Training Opportunities

Abstract Background There is evidence that disability acquisition causes a decline in mental health, but few studies have examined the causal mechanisms through which the effect operates. This study used a novel approach to mediation analysis to quantify interventional indirect effects through employment and income. Methods We used four waves of longitudinal data to compare self-reported mental health between working aged individuals who acquired a disability (n = 233) and those who remained disability-free (n = 5419). We conducted a causal mediation analysis quantifying interventional indirect effects of disability acquisition on mental health operating through two distinct mediators: employment status and income. We used multiple imputation with 50 imputed datasets to account for missing data. Results The total causal effect of disability acquisition on mental health was estimated to be a 4.8-point decline in mental health score (estimated mean difference: -4.8, 95% CI -7.0, -2.7). The interventional indirect effect through employment was estimated to be a 0.5-point difference (-0.5, 95% CI -1.0, 0.0), accounting for 10.6% of the total effect, whereas there was no evidence that income explained any of the effect. Conclusion This study estimated that disability-related mental health inequalities could be reduced by 10.6% if employment rates were the same for people with disabilities as those without. The results highlight the need to implement measures to enable people with disabilities to remain in employment and improve employment and vocational training opportunities for people who acquire a disability.

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