vigipy

vigipy is a project to bring modern disproportionality analyses and pharmacovigilance techniques into the Python ecosystem with a simple, intuitive interface. Many of the disproportionality functions are adapted and extended versions from Ismail Ahmed and Antoine Poncet's amazing work. Top-level functions:

• bcpnn() - Bayesian confidence propogation neural network
• gps() - Multi-item gamma poisson shrinker
• prr() - Proportional reporting ratio
• ror() - Reporting odds ratio
• rfet() - Reporting fisher's exact test
• LongitudinalModel() - Apply any model across time to view signal evolution
• convert() - Convert a table of AEs, product and counts into a format for analysis

Getting Started

Dependencies

For vigipy

• pandas
• numpy
• scipy
• sympy
• statsmodels
• patsy

Installation

To install, navigate to the root directory of the repository and from the command line/terminal run:

python setup.py bdist_wheel
pip install dist\vigipy-1.0.0-py3-none-any.whl

You should now be able to import the vigipy library in your code.

Unit Tests

From the root directory of the repository, run:

python -m unittest discover -s test -p "*Test.py"

Usage

Load data and apply model

from vigipy import *
import pandas as pd

#This is expected to have columns: ['AE', 'name', 'count'] ('date' is optional for longitudinal models)
df = pd.read_csv('AE_count_data.csv')
vivipy_data = convert(df)

#My personal favorite model to run. With 'log2' or 'quantile' as the statistic.
results = gps(vigipy_data, min_events=5, decision_metric='rank',
              decision_thres=1, ranking_statistic='log2')
results.signals.to_excel('possible_signals.xlsx', index=False)

Changing Expected Reporting Calculation

The expected count calculation is an important factor in both the accuracy and stability of any DA algorithm. Vigipy support the use of 3 expectation method: mantel-haentzel, poisson, and negative-binomial. Recent research shows BCPNN performs very well in most DA scenarios (https://onlinelibrary.wiley.com/doi/10.1002/pds.4970) and that LASSO is very robust againt confounding variables. To combine these approaches in vigipy, use the negative-binomial expectation argument in BCPNN. In the event of significant dispersion in the data, you can calculate alpha using test_dispersion

from vigipy import convert
from vigipy.utils import test_dispersion

df = pd.read_csv('AE_count_data.csv')
data_container = convert(df)

dispersion_data = test_dispersion(data_container)
alpha = dispersion_data["alpha"] if dispersion_data["dispersion"] > 2 else 1

bcpnn(data_container, expected_method='negative-binomial', method_alpha=alpha, min_events=3)

Making any model longitudinal:

from vigipy import *
import pandas as pd

df = pd.read_csv('AE_count_data.csv')
#Apply model to each calendar year, cumulative
LM = LongitudinalModel(df, 'A')
LM.run(gps, False, decision_metric='rank', ranking_statistic='quantile')

#Change model time slice to quarterly
LM.regroup_dates('Q')
LM.run(gps, False, decision_metric='rank', ranking_statistic='quantile')

#LM produces a list of timestamps and results
for timestamp, result in LM.results:
    print("Signals produced prior to {0}:".format(timestamp))
    print(result.signals.head())

TODO

• Create a data set for demonstrating usage more thoroughly and to run tests
• Improve high-level documentation of the methods and their parameters
• Integrate with mmappy and spotlight (coming soon) for an end-to-end device surveillance/pharmacovigilance pipeline

Authors

David Beery

License

GNU GPLv3

Acknowledgements

• Ismail Ahmed and Antoine Poncet for the original leg work in designing and implementing a good disproportionality analysis library in R
• Ross Ihaka for his implementation of the log|gamma(x)| function
• Catherine Loader for her Stirling's formula log error and deviance functions.