tmle3mopttx
Targeted Learning and Variable Importance with Optimal Individualized Categorical Treatment
Authors: Ivana Malenica, Jeremy R. Coyle and Mark J. van der Laan
Suppose one wishes to maximize (or minimize) the population mean of an outcome where for each individual one has access to measured baseline covariates. We consider estimation of the mean outcome under the optimal rule, where the candidate rules are restricted to depend only on user-supplied subset of the baseline covariates (or no covariates specified). The estimation problem is addressed in a statistical model for the data distribution that is nonparametric.
The tmle3mopttx
is an adapter/extension R package in the tlverse
ecosystem, that estimates the mean outcome under the following regimes:
1) Optimal Individualized Treatment for categorical treatment, 2) Optimal Individualized Treatment based on possibly sub-optimal rules (including static rules), 3) Optimal Individualized Treatment based on realistic rules. 4) Optimal Individualized Treatment with resource constraints.
The tmle3mopttx
also provides variable importance analysis in
terms of optimizing the population mean of an outcome for settings
(1)-(3). It also can return an interpretable rule based on a Highly
Adaptive Lasso fit.
In addition, tmle3mopttx
supports estimating the mean outcome under
regimes under the following missigness process:
In order to avoid nested cross-validation, tmle3mopttx
relies on
split-specific estimates of the conditional expectation of the outcome
()
and conditional probability of treatment
()
in order to estimate the rule. The targeted maximum likelihood estimates
of the mean performance under the estimated rule are obtained using
CV-TMLE.
The description of the method implemented, with simulations and data
examples using tmle3mopttx
can be found in Malenica (2021). For
additional background on Targeted Learning and previous work on optimal
individualized treatment regimes, please consider consulting van der
Laan, Polley, and Hubbard (2007), Zheng and van der Laan (2010), van der
Laan and Rose (2011), van der Laan and Luedtke (2015), Luedtke and van
der Laan (2016), Coyle (2017) and van der Laan and Rose (2018).
You can install the most recent stable release from GitHub via
devtools
with:
devtools::install_github("tlverse/tmle3mopttx")
After using the tmle3mopttx R package, please cite the following:
@software{malenica2022tmle3mopttx,
author = {Malenica, Ivana and Coyle, Jeremy and {van der Laan}, Mark J},
title = {{tmle3mopttx}: Targeted Learning and Variable Importance with Optimal Individualized Categorical Treatment},
year = {2022},
doi = {},
url = {https://github.com/tlverse/tmle3mopttx},
note = {R package version 1.0.0}
}
If you encounter any bugs or have any specific feature requests, please file an issue.
The contents of this repository are distributed under the GPL-3 license.
See file LICENSE
for details.
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