mediation_analysis_designer {DesignLibrary} | R Documentation |
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
)
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. |
See vignette online.
A mediation analysis design.
# 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)