Causal mediation analysis

Estimate direct, indirect, total, and proportion mediated effects from regression models.

Usage

mediation_analysis(
  data,
  exposure,
  mediator,
  outcome,
  covariates = NULL,
  mediator_approach = "linear",
  outcome_approach = "linear",
  exposure_value = NULL,
  reference_value = NULL,
  sims = 1000,
  conf_level = 0.95,
  seed = NULL,
  format = c("flextable", "gt"),
  theme = c("minimal")
)

Arguments

data

A data frame.

exposure

Exposure/treatment variable. Quoted and bare names are accepted.

mediator

Mediator variable. Quoted and bare names are accepted.

outcome

Outcome variable. Quoted and bare names are accepted.

covariates

Optional character vector of covariate names. Quoted names are recommended in scripts, and bare names are also accepted.

mediator_approach

Mediator model. Currently "linear".

outcome_approach

Outcome model. One of "linear" or "logit". Logistic mediation effects are reported on the predicted probability-difference scale.

exposure_value

Exposure value used as the treatment level. If NULL, the second factor level, value 1 for 0/1 variables, or the 75th percentile for continuous exposures is used.

reference_value

Exposure value used as the reference level. If NULL, the first factor level, value 0 for 0/1 variables, or the 25th percentile for continuous exposures is used.

sims

Number of non-parametric bootstrap replicates used for confidence intervals.

conf_level

Confidence level for intervals.

seed

Optional random seed for reproducible bootstrap intervals.

format

One of "flextable" (default) or "gt".

theme

Table theme.

Value

A list of class c("gtregression", "mediation_analysis", ...) with elements:

table

A formatted flextable or gt_tbl.

table_body

Data frame of mediation effect estimates.

table_display

Formatted data frame used to build the table.

models

Fitted mediator and outcome models.

boot

Bootstrap replicate estimates.

values

Reference and exposure values used.

variable_labels

Named character vector of display labels.

call

Matched function call.

Details

This function is for planned mediation questions, not automatic causal discovery. Causal interpretation requires the usual mediation assumptions, including no unmeasured exposure-outcome, exposure-mediator, or mediator-outcome confounding, correct temporal order, and suitable model specification. Use a directed acyclic graph (DAG) and subject-matter knowledge before interpreting the estimates causally.

For outcome_approach = "linear", effects are mean differences. For outcome_approach = "logit", effects are predicted probability differences, not odds ratios.

Examples

med <- mediation_analysis(
  data = data_diabetes_mediation,
  exposure = obesity,
  mediator = glucose,
  outcome = diabetes,
  covariates = c(age, blood_pressure, pregnancies, diabetes_pedigree),
  outcome_approach = logit,
  sims = 50,
  seed = 123
)

med$table
Effect
Estimate
95% CI
p-value
Interpretation
Total effect
0.268
0.206 to 0.347
<0.001
Overall exposure-outcome association
Direct effect
0.200
0.136 to 0.271
<0.001
Association not through the mediator
Indirect effect
0.068
0.033 to 0.110
<0.001
Association through the mediator
Proportion mediated
0.255
0.126 to 0.395
<0.001
Share of total effect through the mediator
Effects are predicted probability differences from logistic outcome models.
Comparison: Obesity = Yes vs No; mediator = Plasma glucose; outcome = Diabetes. Bootstrap replicates = 50.
Adjusted for Age, Diastolic blood pressure, Number of pregnancies, Diabetes pedigree function.
Causal interpretation requires DAG-supported no-unmeasured-confounding and correct temporal-order assumptions.
med$table_body
#>                effect              Effect   estimate   conf.low conf.high
#> total           total        Total effect 0.26811037 0.20563604 0.3470686
#> direct         direct       Direct effect 0.19978127 0.13629337 0.2710736
#> indirect     indirect     Indirect effect 0.06832911 0.03280945 0.1100138
#> proportion proportion Proportion mediated 0.25485440 0.12640396 0.3954078
#>            p.value                             Interpretation
#> total            0       Overall exposure-outcome association
#> direct           0       Association not through the mediator
#> indirect         0           Association through the mediator
#> proportion       0 Share of total effect through the mediator

# HTML-first output
mediation_analysis(
  data = data_diabetes_mediation,
  exposure = obesity,
  mediator = glucose,
  outcome = diabetes,
  covariates = c(age, blood_pressure, pregnancies, diabetes_pedigree),
  outcome_approach = logit,
  format = gt,
  sims = 50,
  seed = 123
)$table


  
Effect
      
Estimate
      
95% CI
      
p-value
      
Interpretation
    
Total effect
0.268
0.206 to 0.347
<0.001
Overall exposure-outcome association
Direct effect
0.200
0.136 to 0.271
<0.001
Association not through the mediator
Indirect effect
0.068
0.033 to 0.110
<0.001
Association through the mediator
Proportion mediated
0.255
0.126 to 0.395
<0.001
Share of total effect through the mediator
Effects are predicted probability differences from logistic outcome models.
    
Comparison: Obesity = Yes vs No; mediator = Plasma glucose; outcome = Diabetes. Bootstrap replicates = 50.
    
Adjusted for Age, Diastolic blood pressure, Number of pregnancies, Diabetes pedigree function.
    
Causal interpretation requires DAG-supported no-unmeasured-confounding and correct temporal-order assumptions.