Targeted Learning with Moderated Statistics for Biomarker Discovery

Authors: Nima Hejazi, Mark van der Laan, and Alan Hubbard

The biotmle R package facilitates biomarker discovery through a generalization of the moderated t-statistic (Smyth 2004) that extends the procedure to locally efficient estimators of asymptotically linear target parameters (Tsiatis 2007). The set of methods implemented modify targeted maximum likelihood (TML) estimators of statistical (or causal) target parameters (e.g., average treatment effect) to apply variance moderation to the standard variance estimator based on the efficient influence function (EIF) of the target parameter (van der Laan and Rose 2011, 2018). By performing a moderated hypothesis test that pools the individual probe-specific EIF-based variance estimates, a robust variance estimator is constructed, which stabilizes the standard error estimates and improves the performance of such estimators both in smaller samples and in settings where the EIF is poorly estimated. The resultant procedure allows for the construction of conservative hypothesis tests that reduce the false discovery rate and/or the family-wise error rate (Hejazi, van der Laan, and Hubbard 2021). Improvements upon prior TML-based approaches to biomarker discovery (e.g., Bembom et al. (2009)) include both the moderated variance estimator as well as the use of conservative reference distributions for the corresponding moderated test statistics (e.g., logistic distribution), inspired by tail bounds based on concentration inequalities (Rosenblum and van der Laan 2009); the latter prove critical for obtaining robust inference when the finite-sample distribution of the estimator deviates from normality.

For standard use, install from Bioconductor using BiocManager:

if (!requireNamespace("BiocManager", quietly=TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("biotmle")

To contribute, install the bleeding-edge development version from GitHub via remotes:

remotes::install_github("nhejazi/biotmle")

Current and prior Bioconductor releases are available under branches with numbers prefixed by “RELEASE_”. For example, to install the version of this package available via Bioconductor 3.6, use

remotes::install_github("nhejazi/biotmle", ref = "RELEASE_3_6")

For details on how to best use the biotmle R package, please consult the most recent package vignette available through the Bioconductor project.

If you encounter any bugs or have any specific feature requests, please file an issue.

Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.

After using the biotmle R package, please cite both of the following:

    @article{hejazi2017biotmle,
      author = {Hejazi, Nima S and Cai, Weixin and Hubbard, Alan E},
      title = {biotmle: Targeted Learning for Biomarker Discovery},
      journal = {The Journal of Open Source Software},
      volume = {2},
      number = {15},
      month = {July},
      year  = {2017},
      publisher = {The Open Journal},
      doi = {10.21105/joss.00295},
      url = {https://doi.org/10.21105/joss.00295}
    }

    @article{hejazi2021generalization,
      author = {Hejazi, Nima S and Boileau, Philippe and {van der Laan},
        Mark J and Hubbard, Alan E},
      title = {A generalization of moderated statistics to data adaptive
        semiparametric estimation in high-dimensional biology},
      journal={under review},
      volume={},
      number={},
      pages={},
      year = {2021+},
      publisher={},
      doi = {},
      url = {https://arxiv.org/abs/1710.05451}
    }

    @manual{hejazi2019biotmlebioc,
      author = {Hejazi, Nima S and {van der Laan}, Mark J and Hubbard, Alan
        E},
      title = {{biotmle}: {Targeted Learning} with moderated statistics for
        biomarker discovery},
      doi = {10.18129/B9.bioc.biotmle},
      url = {https://bioconductor.org/packages/biotmle},
      note = {R package version 1.10.0}
    }

• R/biotmleData - R package with example experimental data for use with this analysis package.

The development of this software was supported in part through grants from the National Institutes of Health: P42 ES004705-29 and R01 ES021369-05.

© 2016-2021 Nima S. Hejazi

The contents of this repository are distributed under the MIT license. See file LICENSE for details.