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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:
- It is easy to compute.
- It considers AE relationships.
- It depends on data-driven cutoffs.
- 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
Maintainers
Documentation
The documentation is hosted at -
https://niuniular.github.io/MDDC/index.html
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},
}
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.