Create a design for mediation analysis

Source: R/mediation_analysis_designer.R

A mediation analysis design that examines the effect of treatment (Z) on mediator (M) and the effect of mediator (M) on outcome (Y) (given Z=0) as well as direct effect of treatment (Z) on outcome (Y) (given M=0). Analysis is implemented using an interacted regression model. Note this model is not guaranteed to be unbiased despite randomization of Z because of possible violations of sequential ignorability.

mediation_analysis_designer(
  N = 200,
  a = 1,
  b = 0.4,
  c = 0,
  d = 0.5,
  rho = 0,
  args_to_fix = NULL
)

Arguments

N

An integer. Size of sample.
a

A number. Parameter governing effect of treatment (Z) on mediator (M).
b

A number. Effect of mediator (M) on outcome (Y) when Z = 0.
c

A number. Interaction between mediator (M) and (Z) for outcome (Y).
d

A number. Direct effect of treatment (Z) on outcome (Y), when M = 0.
rho

A number in [-1,1]. Correlation between mediator (M) and outcome (Y) error terms. Non zero correlation implies a violation of sequential ignorability.
args_to_fix

A character vector. Names of arguments to be args_to_fix in design.

Value

A mediation analysis design.

Details

See vignette online.

Author

DeclareDesign Team

Examples

# Generate a mediation analysis design using default arguments:
mediation_1 <- mediation_analysis_designer()
draw_inquiries(mediation_1)

#>         inquiry_label estimand
#> 1          FirstStage    0.365
#> 2          Indirect_0    0.400
#> 3          Indirect_1    0.400
#> 4 Controlled_Direct_0    0.500
#> 5 Controlled_Direct_1    0.500
#> 6    Natural_Direct_0    0.500
#> 7    Natural_Direct_1    0.500
if (FALSE) {
diagnose_design(mediation_1, sims = 1000)
}

# A design with a violation of sequential ignorability and heterogeneous effects:
mediation_2 <- mediation_analysis_designer(a = 1, rho = .5, c = 1, d = .75)
draw_inquiries(mediation_2)

#>         inquiry_label estimand
#> 1          FirstStage    0.335
#> 2          Indirect_0    0.400
#> 3          Indirect_1    1.400
#> 4 Controlled_Direct_0    0.750
#> 5 Controlled_Direct_1    1.750
#> 6    Natural_Direct_0    1.275
#> 7    Natural_Direct_1    1.610
if (FALSE) {
diagnose_design(mediation_2, sims = 1000)
}