mediation_analysis_designer: Create a design for mediation analysis

In DesignLibrary: Library of Research Designs

Description

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.

Usage

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.

Details

See vignette online.

Value

A mediation analysis design.

Author(s)

DeclareDesign Team

Examples

# Generate a mediation analysis design using default arguments:
mediation_1 <- mediation_analysis_designer()
draw_estimands(mediation_1)
## Not run: 
diagnose_design(mediation_1, sims = 1000)

## End(Not run)

# 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_estimands(mediation_2)
## Not run: 
diagnose_design(mediation_2, sims = 1000)

## End(Not run)

DesignLibrary documentation built on Oct. 18, 2021, 5:07 p.m.