R/regmedintDS.R

In datashield/dsMediation: DataSHIELD Server side Mediation Analysis Functions

#'
#' @title Regression-based causal mediation analysis
#' @description This function is similar to R function \code{regmedint} from the 
#' \code{regmedint} package.
#' @details The function 'regmedint' is used for regression-based causal mediation
#' analysis as described in Valeri & VanderWeele 2013 and Valeri & VanderWeele 2015.
#' @param data a string character, the name of the data frame containing the 
#' relevant variables.
#' @param yvar a character vector of length 1. Outcome variable name. It should be
#' the time variable for survival outcomes.
#' @param avar a character vector of length 1. Treatment variable name.
#' @param mvar a character vector of length 1. Mediator variable name.
#' @param cvar a character vector of length > 0. Covariate names. Use NULL if
#' there is no covariate. However, this is a highly suspicious situation. Even if
#' avar is randomized, mvar is not. Thus, there should usually be some confounder(s)
#' to account for the common cause structure (confounding) between mvar and yvar. 
#' @param eventvar a character vector of length 1. Only required for survival outcome
#' regression models. Note that the coding is 1 for event and 0 for censoring, 
#' following the R survival package convention.
#' @param a0 a numeric vector of length 1. Reference level of treatment variable that
#' is considered "untreated" or "unexposed".
#' @param a1 a numeric vector of length 1.
#' @param m_cde a numeric vector of length 1. Mediator level at which controlled direct
#' effect is evaluated at.
#' @param c_cond a numeric vector of the same length as cvar. Covariate vector at which 
#' conditional effects are evaluated at.
#' @param mreg a character vector of length 1. Mediator regression type: "linear" or "logistic".
#' @param yreg a character vector of length 1. Outcome regression type: "linear", "logistic", 
#' "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull".
#' @param interaction a logical vector of length 1. Default to TRUE. Whether to include a 
#' mediator-treatment interaction term in the outcome regression model.
#' @param casecontrol a logical vector of length 1. Default to FALSE. Whether data comes from
#' a case-control study.
#' @param na_omit a logical vector of length 1. Default to FALSE. Whether to use na.omit() function
#' in stats package to remove NAs in columns of interest before fitting the models.
#' @param exponentiate a logical vector of length 1. Default to FALSE. Whether to exponentiate
#' the estimates to measure the odds ratio incase of a logistic regression model 
#' @return a summary table of the object of class 'multimed'.
#' @author Demetris Avraam, for DataSHIELD Development Team
#' @import regmedint
#' @export
#'
regmedintDS <- function(data, yvar, avar, mvar, cvar, eventvar, a0, a1, m_cde, c_cond, mreg, yreg,
                        interaction = TRUE, casecontrol = FALSE, na_omit = FALSE, exponentiate = FALSE){
  
  data <- eval(parse(text=data), envir = parent.frame())
  
  if(!is.null(cvar)){
    cvar <- unlist(strsplit(cvar, split=","))
  }
  
  if(!is.null(c_cond)){
    c_cond <- as.numeric(unlist(strsplit(c_cond, split=",")))
  }
  
  if(!is.null(cvar)){
    regmedint.out <- regmedint::regmedint(data = data, yvar = yvar, avar = avar, mvar = mvar, 
                                        cvar = cvar, eventvar = eventvar, a0 = a0, a1 = a1, 
                                        m_cde = m_cde, c_cond = c_cond, mreg = mreg, yreg = yreg,
                                        interaction = interaction, casecontrol = casecontrol, 
                                        na_omit = na_omit)
  }else{
    regmedint.out <- regmedint::regmedint(data = data, yvar = yvar, avar = avar, mvar = mvar, 
                                          cvar = c(), eventvar = eventvar, a0 = a0, a1 = a1, 
                                          m_cde = m_cde, c_cond = c(), mreg = mreg, yreg = yreg, 
                                          interaction = interaction, casecontrol = casecontrol, 
                                          na_omit = na_omit)    
  }
  
  out <- summary(regmedint.out, exponentiate = exponentiate)
  return(out)
  
}
# AGGREGATE FUNCTION
# regmedintDS