more.effects: Estimate additional natural direct and indirect effects based on an `"effectsMed"` object

Description

Takes an "effectsMed" object and estimates additional natural direct and indirect effects, with a sensitivity analysis using the same sensitivity parameter as in the original analysis, without having to redo the optimization to find the estimated regression coefficients. The effects to be estimated are regulated through the arguments covariates, alt.decomposition, exp.value and control.value as described in the documentation for sensmediation. The confidence level used is regulated through the argument conf.level.

Usage

more.effects(
  sensmed.object,
  conf.level = 0.95,
  covariates = NULL,
  alt.decomposition = FALSE,
  exp.value = NULL,
  control.value = NULL
)

Value

more.effects returns an object of class "effectsMed", see the documentation for sensmediation

for information.

Arguments

sensmed.object: an object of class "effectsMed" for which additional effects are to be calculated.
conf.level: the confidence level to be used for confidence intervals and uncertainty intervals.
covariates: if conditional effects are to be estimated the list of covariate values (see sensmediation). Covariates not specified are marginalized over.
alt.decomposition: logical indicating whether alternative definitions of the direct and indirect effects should be used (see sensmediation).
exp.value: value of the exposure variable used as the exposure condition, default is to take the value stored in sensmed.object.
control.value: value of the exposure variable used as the control (unexposed) condition, default is to take the value stored in sensmed.object.

Author

Anita Lindmark

Examples

Run this code

if (FALSE) {

# Example with data from Riksstroke (the Swedish stroke register)

data(RSdata)

# Probit mediator and outcome models:
med.model <- glm(lowered.consc ~ AF + age.cat + sex, data = RSdata,
family = binomial(link = 'probit'))
out.model <- glm(cf.3mo ~ AF + lowered.consc + age.cat + sex, data = RSdata,
family = binomial(link = 'probit'))

# First we estimate marginal NIE, NDE with sensitivity analyses to mediator-outcome
# confounding:
sensmed <- sensmediation(med.model, out.model, exp.name = "AF1", med.name = "lowered.consc",
Rho = seq(0, 0.5, 0.1))

# Then we also estimate NIE, NDE conditional on male sex without reestimating the regression
# coefficients:
sensmed.cond <- more.effects(sensmed.object = sensmed, covariates = list(sex = 1))
summary(sensmed.cond)
plot(sensmed.cond)
}