In ck37/tlmixture: Data-adaptive Creation Of Exposure (Treatment) Mixtures Using Targeted Learning

knitr::opts_chunk$set(
  collapse = TRUE,
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tlmixture

tlmixture is an R package to construct mixtures of groups of correlated exposures (treatments) and estimate the relationship between the mixture and an outcome.

Installation

You can install the development version of tlmixture from GitHub:

if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("ck37/tlmixture")

Example

This is a simple example which shows how to use some basic function arguments.

library(tlmixture)

# Basic example code
result =
  tlmixture(
            # Dataframe containing outcome, exposures, and adjustment variables.
            data,
            # Name of the outcome variable.
            outcome = "y",
            # Vector of exposure names (single group), or a list with separate vectors per group.
            exposures = c("exposure1", "exposure2", "exposure3")
            # This will evaluate mixtures at low/medium/high levels.
            quantiles_mixtures = 3,
            # This SuperLearner library will be used for propensity too.
            estimator_outcome = c("SL.mean", "SL.glmnet", "SL.ranger")
            # How many CV-TMLE folds to use; more is generally better, but slower to compute.
            folds_cvtmle = 3)

# Review parameter estimates and confidence intervals.
result$combined$results

ck37/tlmixture documentation built on Dec. 19, 2021, 5:13 p.m.