---

output:

  rmarkdown::github_document

bibliography: "inst/REFERENCES.bib"

---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# R/`biotmle`

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> Targeted Learning with Moderated Statistics for Biomarker Discovery

__Authors:__ [Nima Hejazi](https://nimahejazi.org), [Mark van der

Laan](https://vanderlaan-lab.org/about), and [Alan

Hubbard](https://hubbard.berkeley.edu)

---

## What's `biotmle`?

The `biotmle` R package facilitates biomarker discovery through a generalization

of the moderated t-statistic [@smyth2004linear] that extends the procedure to

locally efficient estimators of asymptotically linear target parameters

[@tsiatis2007semiparametric]. 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 [@vdl2011targeted; @vdl2018targeted]. 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

[@hejazi2021generalization]. Improvements upon prior TML-based approaches to

biomarker discovery (e.g., @bembom2009biomarker) 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 [@rosenblum2009confidence]; the latter prove critical for obtaining

robust inference when the finite-sample distribution of the estimator deviates

from normality.

---

## Installation

For standard use, install from

[Bioconductor](https://bioconductor.org/packages/biotmle) using

[`BiocManager`](https://CRAN.R-project.org/package=BiocManager):

```{r bioc-installation, eval = FALSE}

if (!requireNamespace("BiocManager", quietly=TRUE)) {

  install.packages("BiocManager")

}

BiocManager::install("biotmle")

```

To contribute, install the bleeding-edge _development version_ from GitHub via

[`remotes`](https://CRAN.R-project.org/package=remotes):

```{r gh-master-installation, eval = FALSE}

remotes::install_github("nhejazi/biotmle")

```

Current and prior [Bioconductor](https://bioconductor.org) 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

```{r gh-develop-installation, eval = FALSE}

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

```

---

## Example

For details on how to best use the `biotmle` R package, please consult the most

recent [package

vignette](https://bioconductor.org/packages/release/bioc/vignettes/biotmle/inst/doc/exposureBiomarkers.html)

available through the [Bioconductor

project](https://bioconductor.org/packages/biotmle).

---

## Issues

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

issue](https://github.com/nhejazi/biotmle/issues).

---

## Contributions

Contributions are very welcome. Interested contributors should consult our

[contribution

guidelines](https://github.com/nhejazi/biotmle/blob/master/CONTRIBUTING.md)

prior to submitting a pull request.

---

## Citation

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}

        }

---

## Related

* [R/`biotmleData`](https://github.com/nhejazi/biotmleData) - R package with

    example experimental data for use with this analysis package.

---

## Funding

The development of this software was supported in part through grants from the

National Institutes of Health: [P42 ES004705-29](https://projectreporter.nih.gov/project_info_details.cfm?aid=9260357&map=y) and [R01 ES021369-05](https://projectreporter.nih.gov/project_info_description.cfm?aid=9210551&icde=37849782&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball=).

---

## License

© 2016-2021 [Nima S. Hejazi](https://nimahejazi.org)

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

`LICENSE` for details.

---

## References