Package 'multimediate'

Title: Causal Mediation Analysis in Presence of Multiple Mediators Uncausally Related
Description: Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jerolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) <doi:10.1101/2024.02.16.24302923>.
Authors: Allan Jerolon [aut], Arce Domingo-Relloso [aut], Samara Kiihl [cre, aut], Maria Tellez-Plaza [aut]
Maintainer: Samara Kiihl <[email protected]>
License: GPL-3
Version: 0.1.6
Built: 2026-05-22 17:19:59 UTC
Source: https://github.com/samarafk/multimediate

Help Index

Toy dataset 1

Description

A toy dataset for causal mediation analysis with 3 uncausally related mediators.

Usage

data("data1")

Format

A data frame with 1000 observations on the following 8 variables.

Treatment

a factor with levels 0 1

C1

a factor with levels 0 1

C2

a factor with levels 0 1

C3

a factor with levels 0 1

M1

a numeric vector

M2

a numeric vector

M3

a numeric vector

Outcome

a numeric vector

Toy dataset 2

Description

A toy dataset for causal mediation analysis with 3 uncausally related mediators.

Usage

data("data2")

Format

A data frame with 1000 observations on the following 8 variables.

Treatment

a factor with levels 0 1

C1

a factor with levels 0 1

C2

a factor with levels 0 1

C3

a factor with levels 0 1

M1

a numeric vector

M2

a numeric vector

M3

a numeric vector

Outcome

a factor with levels FALSE TRUE

Toy dataset 3

Description

A toy dataset for causal mediation analysis with 3 uncausally related mediators.

Usage

data("data3")

Format

A data frame with 1000 observations on the following 8 variables.

Treatment

a factor with levels 0 1

C1

a factor with levels 0 1

C2

a factor with levels 0 1

C3

a factor with levels 0 1

M1

a numeric vector

M2

a numeric vector

M3

a numeric vector

Outcome

a factor with levels 0 1 2 3

Toy dataset 4

Description

A toy dataset for causal mediation analysis with 3 uncausally related mediators.

Usage

data("data4")

Format

A data frame with 1000 observations on the following 8 variables.

Treatment

a numeric vector

C1

a factor with levels 0 1

C2

a factor with levels 0 1

C3

a factor with levels 0 1

M1

a numeric vector

M2

a factor with levels 0 1

M3

a factor with levels 0 1 2 3

Outcome

a numeric vector

Toy dataset 5

Description

A toy dataset for causal mediation analysis with 3 uncausally related mediators and survival outcome.

Usage

data("data5")

Format

A data frame with 3000 observations on the following 8 variables.

Exposure

a numeric vector

M1

a numeric vector

M2

a numeric vector

M3

a numeric vector

event

event

surv_time

time before event

multimediate

Description

multimediate estimates several quantities in causal mediation analysis, including the average causal mediation effect (indirect effect), average direct effect, proportion mediated, and total effect, in the presence of multiple uncausally related mediators.

Usage

multimediate(
  lmodel.m,
  correlated = FALSE,
  model.y,
  treat,
  treat.value = 1,
  control.value = 0,
  J = 1000,
  conf.level = 0.95,
  fun = mean,
  data = NULL,
  peryr = 1e+05,
  verbose = TRUE
)

Arguments

lmodel.m

list of fitted models object for mediators. Can be of class lm, polr, glm.
correlated

a logical value. if FALSE a identity matrix is used for the matrix of correlation of mediators; if TRUE matrix of correlation is estimated. Default is 'FALSE'.
model.y

a fitted model object for the outcome. Can be of class lm, polr, glm or aalen.
treat

a character string indicating the name of the treatment variable used in the models. The treatment can be either binary (integer or a two-valued factor) or continuous (numeric).
treat.value

value of the treatment variable used as the treatment condition. Default is 1.
control.value

value of the treatment variable used as the control condition. Default is 0.
J

number of Monte Carlo draws for quasi-Bayesian approximation.
conf.level

level of the returned two-sided confidence intervals. Default is to return the 2.5 and 97.5 percentiles of the simulated quantities.
fun

the function used to compute the point estimate of the effects of interest from its empirical distribution. The function mean or median can be used. Default is the function mean.
data

dataset with all variables used in the mediator and outcome models
peryr

the number of person-years to multiply the additive estimator to obtain results in scale of cases per person-years (only applicable to the survival outcome)
verbose

Logical. If TRUE (default), messages and a progress bar are displayed during execution.

Details

For binary outcomes, it also estimates average causal mediation effects on the odds ratio (OR) and log-odds (logOR) scales.

For survival outcomes, multimediate additionally estimates average causal mediation effects on cases per person-year scale.

Value

multimediate returns an object of class mm, a list that contains at least the components listed below. The function summary (i.e., summary.mm) can be used to obtain a table of the results.

  • model.y: the fitted outcome model.

  • model.m: a list of fitted mediator models, one for each mediator.

  • treatment: a character string indicating the name of the treatment variable used in the models.

  • treat.value: value of the treatment variable used as the treatment condition.

  • control.value: value of the treatment variable used as the control condition.

  • sims: the number of Monte Carlo draws for quasi-Bayesian approximation.

Examples

# Load example data
data(data1)
summary(data1)

# Fit mediator models
M1reg <- lm(M1 ~ Treatment + C1 + C2, data = data1)
M2reg <- lm(M2 ~ Treatment + C1 + C3, data = data1)
M3reg <- lm(M3 ~ Treatment + C1, data = data1)

# Fit outcome model
Yreg <- lm(Outcome ~ Treatment + M1 + M2 + M3 + C1 + C2 + C3, data = data1)

# Perform multiple mediation analysis (may take some time)

med.analysis <- multimediate(
  lmodel.m = list(M1reg, M2reg, M3reg),
  correlated = TRUE,
  model.y = Yreg,
  treat = "Treatment",
  treat.value = 1,
  control.value = 0,
  J = 100,
  conf.level = 0.95,
  verbose = FALSE
)

summary(med.analysis, opt = "avg")

summary.mm

Description

summary.mm is used to display the results of the mediation analyzes done with multimediate.

Usage

## S3 method for class 'mm'
summary(object, opt = "navg", logit = "all", ...)

Arguments

object

element of the class mm.
opt

a character string indicating the details of the analysis "navg" for the average causal effects for t=0,1 and "avg" for the average causal effects.
logit

a character string indicating, when the outcome is binary, the scale of the average causal effects. "effects" for average causal effects, " OR" average causal effects on OR scale, "logOR" average causal effects on logOR scale and "all" for all scale.
...

additional arguments affecting the summary produced

Value

table summarizing the causal analysis