MDDC 1.1.0

Methods for detecting signals related to adverse event and medical product (e.g., drugs, vaccines) pairs. This includes a data generation function for simulating pharmacovigilance datasets, along with various utility functions. For more details, please see - Liu A, Mukhopadhyay R, Markatou M (2024). “MDDC: An R and Python Package for Adverse Event Identification in Pharmacovigilance Data.” arXiv preprint. https://doi.org/10.48550/arXiv.2410.01168.

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Introduction

• In this package, we present the Modified Detecting Deviating Cells (MDDC) algorithm for adverse event identification.
• For a certain time period, the spontaneous reports can be extracted from the safety database and depicted as an $I \times J$ contingency table, where:
  • $I$ denotes the total number of AEs
  • $J$ denotes the total number of drugs or vaccines
  • With cell counts $n_{ij}$ the total number of reported cases corresponding to the $j$-th drug/vaccine and $i$-th AE
• We are interested in which (AE, drug or vaccine) pairs are signals. The signals refer to potential adverse events that may be caused by a drug/vaccine.
• In the contingency table setting, the signals refer to the cells with $n_{ij}$ abnormally higher than the expected values.
• Rousseeuw and Bossche (2018) proposed the Detecting Deviating Cells (DDC) algorithm for outlier identification in a multivariate dataset.
• The original DDC algorithm assumes multivariate normality of the data and selects cutoff values based on this assumption. We modify the DDC algorithm to better suit the discrete nature of adverse event data in pharmacovigilance that clearly do not follow a multivariate normal distribution.
• Our Modified Detecting Deviating Cells (MDDC) algorithm has the following characteristics:
  1. It is easy to compute.
  2. It considers AE relationships.
  3. It depends on data-driven cutoffs.
  4. It is independent of the use of ontologies.
• The MDDC algorithm has five steps, with the first two steps identifying univariate outliers via cutoffs, and the next three steps evaluating the signals via the use of AE correlations. The algorithm can be found at MDDC algorithm.

Authors

• Anran Liu Email: [e-mail]

• Raktim Mukhopadhyay Email: [e-mail]

• Marianthi Markatou Email: [e-mail]

Maintainer

Raktim Mukhopadhyay
Email: [e-mail]

Documentation

The documentation is hosted on Read the Docs at - https://mddc.readthedocs.io/en/latest/

Installation using pip

pip install MDDC

Community

For installing the development version, please download the code files from the master branch of the Github repository. Please note that installation from Github might be buggy, for the latest stable release please download using pip. For downloading from Github, use the following instructions:

git clone https://github.com/rmj3197/MDDC.git
cd MDDC
pip install -e .

Contributing Guide

Please refer to the Contributing Guide.

Code of Conduct

The code of conduct can be found at Code of Conduct.

License

This project uses the GPL-3.0 license, with a full version of the license included in the repository.

Citation

If you use this package in your research or work, please cite it as follows:

@misc{liu2024mddcrpythonpackage,
      title={MDDC: An R and Python Package for Adverse Event Identification in Pharmacovigilance Data}, 
      author={Anran Liu and Raktim Mukhopadhyay and Marianthi Markatou},
      year={2024},
      eprint={2410.01168},
      archivePrefix={arXiv},
      primaryClass={stat.CO},
      url={https://arxiv.org/abs/2410.01168}, 
}

Funding Information

The work has been supported by Food and Drug Administration, and Kaleida Health Foundation.

References

Liu, A., Mukhopadhyay, R., and Markatou, M. (2024). MDDC: An R and Python package for adverse event identification in pharmacovigilance data. arXiv preprint. arXiv:2410.01168

Liu, A., Markatou, M., Dang, O., and Ball, R. (2024). Pattern discovery in pharmacovigilance through the Modified Detecting Deviating Cells (MDDC) algorithm. Technical Report, Department of Biostatistics, University at Buffalo.

Rousseeuw, P. J., and Bossche, W. V. D. (2018). Detecting deviating data cells. Technometrics, 60(2), 135-145.