sensitivity: Sensitivity Analysis for Causal Mediation Analysis Using...

In rmpw: Causal Mediation Analysis Using Weighting Approach

Description

This function generates a sensitivity analysis table. When only the mediator-outcome relationship is possibly confounded, the function computes the effect size of actual bias associated with each omitted pretreatment or posttreatment covariate or their combinations; it also computes the effect size of potential bias associated with an unmeasured confounder comparable to an observed pretreatment confounder that was already adjusted for. When the treatment assignment is also subjected to hidden selection bias, the function additionally assesses potential confounding of each omitted or unmeasured pretreatment covariate that may confound both the mediator-outcome relationship and the treatment assignment.

Usage

sensitivity(est.ie, est.de, est.se.ie, est.se.de, outcome, mediator, treatment, 
    X, X.omit.pre = NULL, X.omit.post = NULL, X.unmeasure.pre = NULL, m.scale, 
    t.rand = TRUE, t.confound = FALSE, data)

Arguments

est.ie	The effect size of the original natural indirect effect (NIE) estimate obtained from an RMPW analysis.
est.de	The effect size of the original natural direct effect (NDE) estimate obtained from an RMPW analysis.
est.se.ie	The estimated SE of the effect size of the NIE estimate.
est.se.de	The estimated SE of the effect size of the NDE estimate.
outcome	The name of the outcome variable (string).
mediator	The name of the mediator variable (string).
treatment	The name of the treatment variable (string).
X	A vector of names of the observed pretreatment covariates already adjusted for in the original analysis (string).
X.omit.pre	An optional vector of names of the observed pretreatment covariates that are omitted from the original analysis and may confound the mediator-outcome relationship (string). The default is NULL.
X.omit.post	An optional vector of names of the observed pretreatment covariates that are omitted from the original analysis and may confound the mediator-outcome relationship and preceding the focal mediator (string). The default is NULL. X, X.omit.pre, and X.omit.post are mutually exclusive.
X.unmeasure.pre	An optional vector of names of the observed pretreatment confounders that are already adjusted for in the original analysis and are comparable to some potential unmeasured confounders (String). X.unmeasure.pre is a subset of X.
m.scale	Scale of the mediator ("discrete" or "continuous").
t.rand	A logical value. If TRUE, treatment is randomized. If FALSE, treatment is not randomized. The default is TRUE.
t.confound	A logical value. If TRUE, X.omit.pre may also confound the treatment-mediator or treatment-outcome relationships in addition to confounding the mediator-outcome relationship. The default is FALSE. If X.omit.pre = NULL or z.rand = TRUE, then z.confound = FALSE.
data	A data frame containing the variables in the model.

Value

A sensitivity analysis table that contains rho, sigma, the effect size of the actual bias, the effect size of the modified estimate, and the effect size of the modified confidence interval, associated with each omitted covariate or each combination of omitted covariates, for both NIE and NDE.

Author(s)

Xu Qin, Guanglei Hong, and Fan Yang

References

Hong, G., Qin, X., & Yang, F. (in press). Weighting-based sensitivity analysis in causal mediation studies. Journal of Educational and Behavioral Statistics. doi: 10.3102/1076998617749561

Examples

data(Riverside)
omit.bias = sensitivity(est.ie = -0.111, est.de = 0.158, est.se.ie = 0.059, 
    est.se.de = 0.108, outcome = "trunc_dep12sm2", mediator = "emp", 
    treatment = "treat", X = c("emp_prior", "pqtrunc50", "pqtrunc51", 
        "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic", "pqtrunc49", 
        "nevmar"), X.omit.pre = c("AFDC3660", "pqtrunc25", "nohsdip"), 
    X.omit.post = "AFDC0_Y1", m.scale = "discrete", t.rand = TRUE, t.confound = FALSE, 
    data = Riverside)

Example output

Loading required package: gtools
Loading required package: MASS

rmpw documentation built on May 2, 2019, 8:32 a.m.