Jordan Creed
📆 February 28th, 2020
📍 Moffitt Cancer Center, Tampa, FL
⭐ Abstract
Background: A mediation analysis examines the extent to which the causal relationship between an exposure and outcome operates through an intermediate variable, known as a mediator. A recently developed counterfactual framing of mediation analysis — referred to as causal mediation analysis — extends traditional methods by allowing exposure-mediator interactions and appropriately decomposing the total effect into direct and indirect effects. Current software implementations for causal mediation analysis are available as SAS and SPSS macros by Valeri and VanderWeele. Within R, the package mediation (Tingley et al.) provides a subset of similar functions, but uses a different estimation approach and set of terminology which are less familiar to public health researchers. We present the R package mediator, which provides point estimates and confidence intervals for statistics of interest in a causal mediation analysis. These include the controlled direct effect (CDE), natural direct and indirect effects (NDE and NIE), total effect (TE), and proportion mediated (PM). The package allows the user to specify whether an interaction between the exposure and mediator is assumed to exist. In addition to offering the first R implementation of causal mediation analysis as described by VanderWeele (2015), our implementation offers substantial performance enhancements over existing SAS/SPSS macros.
Usage: The package was developed using R v 3.6.1 and is currently
available on GitHub. Installation and loading is accomplished with
devtools::install_github("gerkelab/mediator"); library(mediator).The
mediator package allows for binary and continuous exposures, mediators
and outcomes, as well assurvival outcomes. Use of the package is
centered around the mediator() function. Minimum inputs for
functionality include the analytic data set (data =), generlized
linear model specifications for the outcome and mediator (out.model =
and med.model =), and the treatment variable (treat =). Additional
options include setting the exposure level (a =), the compared
exposure level (a_star =), the level of the mediator (m =), and the
number of bootstrap replications for calculating confidence intervals
(boot_rep =). Fixed levels of the mediator are used for calculating
CDE, hence, there are as many potential values for the CDE as there are
levels of the mediator. By default the function calculates parametric
confidence intervals using the Delta method, but user specification of
nonzero bootstrap replicates automatically changes the method of
confidence interval estimation to bootstrap. The function returns a
tibble with the CDE, NDE, NIE, TE, and PM, along with 95% confidence
intervals. Returned effects are estimated at either the mean value
(continuous) or the most common value (categorical) of covariates which
are not the mediator or exposure variables.
Conclusion: The mediator R package provides an efficient mechanism for conducting causal mediation analyses and a useful tool in reproducible epidemiologic research.