rmpw {rmpw} | R Documentation |
Causal Mediation Analysis Using Weighting Approach
Description
Causal Mediation Analysis Using Weighting Approach
Usage
rmpw(data, treatment, mediator, outcome, propensity_x, outcome_x, decomposition)
Arguments
data |
The data set for analysis. |
treatment |
The name of the treatment variable (string). |
mediator |
The name of the mediator variable (string). |
outcome |
The name of the outcome variable (string). |
propensity_x |
A vector of variable names (string) of pretreatment confounders, which will be included in the propensity score model. |
outcome_x |
A vector of variable names (string) of pretreatment confounders, which will be included in the outcome model. |
decomposition |
Type of decomposition. When decomposition = 1, the total treatment effect will be decomposed into pure direct effect (DE.0), total and pure indirect effect (IE.1 and IE.0), and natural treatment-by-mediator interaction effect (IE.1 - IE.0). When decomposition = 2, the total treatment effect will be decomposed into pure indirect effect (IE.0), total and pure direct effect (DE.1 and DE.0), and natural treatment-by-mediator interaction effect (DE.1 - DE.0). |
Value
A list contains the estimates of the causal effects and the coefficients of the pretreatment covariates.
Author(s)
Xu Qin and Guanglei Hong
References
Hong, G., Deutsch, J., & Hill, H. D. (2015). Ratio-of-mediator-probability weighting for causal mediation analysis in the presence of treatment-by-mediator interaction. Journal of Educational and Behavioral Statistics, 40 (3), 307-340. doi:10.3102/1076998615583902
Examples
data(Riverside)
rmpw(data = Riverside, treatment = "treat", mediator = "emp", outcome = "trunc_dep12sm2",
propensity_x = c("emp_prior", "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53",
"pqtrunc30", "hispanic", "pqtrunc49", "nevmar"), outcome_x = c("emp_prior",
"pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic",
"pqtrunc49", "nevmar"), decomposition = 0)
rmpw(data = Riverside, treatment = "treat", mediator = "emp", outcome = "trunc_dep12sm2",
propensity_x = c("emp_prior", "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53",
"pqtrunc30", "hispanic", "pqtrunc49", "nevmar"), outcome_x = c("emp_prior",
"pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic",
"pqtrunc49", "nevmar"), decomposition = 1)
rmpw(data = Riverside, treatment = "treat", mediator = "emp", outcome = "trunc_dep12sm2",
propensity_x = c("emp_prior", "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53",
"pqtrunc30", "hispanic", "pqtrunc49", "nevmar"), outcome_x = c("emp_prior",
"pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic",
"pqtrunc49", "nevmar"), decomposition = 2)