`R/mediation_analysis_designer.R`

`mediation_analysis_designer.Rd`

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. |

A mediation analysis design.

See vignette online.

# 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 0.500if (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