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

# 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 (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.

-----

## Installation

For standard use, install from

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

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

``` r

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

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

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

<div id="refs" class="references">

<div id="ref-bembom2009biomarker">

Bembom, Oliver, Maya L Petersen, Soo-Yon Rhee, W Jeffrey Fessel, Sandra

E Sinisi, Robert W Shafer, and Mark J van der Laan. 2009. “Biomarker

Discovery Using Targeted Maximum-Likelihood Estimation: Application to

the Treatment of Antiretroviral-Resistant Hiv Infection.” *Statistics in

Medicine* 28 (1): 152–72.

</div>

<div id="ref-hejazi2021generalization">

Hejazi, Nima S, Mark J van der Laan, and Alan E Hubbard. 2021. “A

Generalization of Moderated Statistics to Data Adaptive Semiparametric

Estimation in High-Dimensional Biology.” *Under Review*.

<https://arxiv.org/abs/1710.05451>.

</div>

<div id="ref-rosenblum2009confidence">

Rosenblum, Michael A, and Mark J van der Laan. 2009. “Confidence

Intervals for the Population Mean Tailored to Small Sample Sizes, with

Applications to Survey Sampling.” *The International Journal of

Biostatistics* 5 (1).

</div>

<div id="ref-smyth2004linear">

Smyth, Gordon K. 2004. “Linear Models and Empirical Bayes Methods for

Assessing Differential Expression in Microarray Experiments.”

*Statistical Applications in Genetics and Molecular Biology* 3 (1):

1–25. <https://doi.org/10.2202/1544-6115.1027>.

</div>

<div id="ref-tsiatis2007semiparametric">

Tsiatis, Anastasios. 2007. *Semiparametric Theory and Missing Data*.

Springer Science & Business Media.

</div>

<div id="ref-vdl2011targeted">

van der Laan, Mark J., and Sherri Rose. 2011. *Targeted Learning: Causal

Inference for Observational and Experimental Data*. Springer Science &

Business Media.

</div>

<div id="ref-vdl2018targeted">

van der Laan, Mark J, and Sherri Rose. 2018. *Targeted Learning in Data

Science: Causal Inference for Complex Longitudinal Studies*. Springer

Science & Business Media.

</div>

</div>