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Data-adaptive creation of exposure (treatment) mixtures using targeted learning

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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.

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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

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Data-adaptive creation of exposure (treatment) mixtures using targeted learning

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