General Purpose g-Estimation for End ofStudy or Time-Varying Outcomes.
Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) and Dukes and Vansteelandt (2018).
The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories.
The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable.
You can install gesttools version 1.3.0 from R using:
install.packages("gesttools")For Further Information. Please refer to the User Manual Provided on GitHub or from CRAN.
We provide a basic example of gesttools with comments below.
#Load the Package
library(gesttools)
#Create a simple dataset with exposure A and outcome Y.
#Format the data using the FormatData() function and generate history of exposure.
datas <- dataexamples(n = 1000, seed = 123, Censoring = FALSE)
data <- datas$datagest
data <- FormatData(
data = data, idvar = "id", timevar = "time", An = "A",
varying = c("Y", "A", "L"), GenerateHistory = TRUE, GenerateHistoryMax = 1)
#Define Inputs
idvar <- "id"
timevar <- "time"
Yn <- "Y"
An <- "A"
Cn <- NA
#Define the outcome models for each of the three exposure times
outcomemodels <- list("Y~A+L+U+Lag1A", "Y~A+L+U+Lag1A", "Y~A+L+U+Lag1A")
#Define the propensity score model of being exposed. Note that time is included
propensitymodel <- c("A~L+U+as.factor(time)+Lag1A")
censoringmodel <- NULL
EfmVar <- NA
#Perform g-estimation
gestSingle(data, idvar, timevar, Yn, An, Cn, outcomemodels, propensitymodel,
censoringmodel = NULL, type = 1, EfmVar)