lmoutzimed {mzipmed}

Mediation Analysis for Zero-Inflated Count Mediators using MZIP (Continuous Outcome)

Description

This function incorporates the MZIP model into the counterfactual approach to mediation analysis as proposed by Vanderweele when the mediator is a Zero-Inflated count variable. Errors for direct and indirect effects are computed using delta method or bootstrap. Note: This function assumes that the outcome is continuous and all exposure, mediator, outcome, and covariates have the same sample size. Binary variables must be dummy coded prior.

Usage

lmoutzimed(
  outcome,
  mediator,
  exposure,
  confounder = NULL,
  C = NULL,
  n = 1000,
  X = 1,
  Xstar = 0,
  error = "Delta",
  robust = FALSE,
  zioff = NULL
)

Arguments

outcome

is the continuous outcome variable

mediator

is the zero-inflated mediator variable, currently only 1 mediator allowed

exposure

is the primary exposure being considered, only 1 is allowed

confounder

is a vector of confounder variables. If no confounder variables are needed then confounder is set to NULL. If more than 1 confounder is being considered then use the cbind function, e.g. cbind(var1,var2)

C

is a vector for theoretical values of each confounder. By default each each value of C will be the mean value of each confounder.

n

is the number of repetition if bootstrapped errors are used

X

is the theoretical value for the exposure variable to be set at. The default is to 1

Xstar

is the theoretical value for the exposure variable to be compared to X. The default is 0, so direct, indirect, and proportion mediated values will be for a 1 unit increase in the exposure variable.

error

='Delta' for delta method standard errors and ='Boot' for bootstrap. Default is delta method

robust

indicates if a robust covariance matrix should be used for MZIP in delta method derivations. Default is FALSE.

zioff

(optional) use to specify an offset variable within the MZIP mediator model. Note: Mediator/Offset is used in the outcome model

Value

The function will return a list of 12 elements. LM is the results of regressing the mediator+exposure+confounder on the outcome using a linear model
MZIP is the results of regressing the exposure and confounders on the mediator using the MZIP model
NDE is the direct effect
NIE is the indirect effect.
NDEse is the standard error for the direct effect
NDEci is the 95% confidence interval for the direct effect
NIEse is the standard error for the indirect effect
NIEci is the 95% confidence interval for the indirect effect
TE is the total effect
TEse is the standard error for the total effect
TECI is the confidence interval for the total effect
PM is the proportion mediated

Examples

    #Example with delta method
    zimed=lmoutzimed(outcome=mzipmed_data$lmY,mediator=mzipmed_data$ziM,
                 exposure=mzipmed_data$X,confounder=cbind(mzipmed_data$C1,
                 mzipmed_data$C2),error="Delta",robust=FALSE,X=1,Xstar=0,zioff=NULL)

    #Example using bootstrapping, 10 iterations used for succinctness
    zimed2=lmoutzimed(outcome=mzipmed_data$lmY,mediator=mzipmed_data$ziM,
                  exposure=mzipmed_data$X,confounder=cbind(mzipmed_data$C1,
                   mzipmed_data$C2),error="Boot",n=10,C=c(0,0.5))