biotmle

biotmle: Targeted Learning for Biomarker Discovery - Published in JOSS (2017)

https://github.com/nhejazi/biotmle

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:package: :microscope: R/biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery

README.Rmd

---
output:
  rmarkdown::github_document
bibliography: "inst/REFERENCES.bib"
---



```{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

Owner

JOSS Publication

biotmle: Targeted Learning for Biomarker Discovery

Published

July 26, 2017

DOI

10.21105/joss.00295

Volume 2, Issue 15, Page 295

Authors

Nima S. Hejazi
Division of Biostatistics, University of California, Berkeley

Weixin Cai
Division of Biostatistics, University of California, Berkeley

Alan E. Hubbard
Division of Biostatistics, University of California, Berkeley

Editor

Karthik Ram

Tags

targeted learning variable importance causal inference bioinformatics genomics

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