https://github.com/pzivich/zEpid
Epidemiology analysis package
https://github.com/pzivich/zEpid
aipw data-science epidemiology epidemiology-analysis g-computation g-estimation g-formula incidence-rate-ratio inverse-probability-weights ipw odds-ratio risk-difference risk-ratio targeted-maximum-likelihood tmle
Last synced: 7 months ago
JSON representation
Epidemiology analysis package
- Host: GitHub
- URL: https://github.com/pzivich/zEpid
- Owner: pzivich
- License: mit
- Created: 2017-10-10T11:47:47.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-05-07T22:07:24.000Z (about 3 years ago)
- Last Synced: 2025-08-31T00:56:58.586Z (10 months ago)
- Topics: aipw, data-science, epidemiology, epidemiology-analysis, g-computation, g-estimation, g-formula, incidence-rate-ratio, inverse-probability-weights, ipw, odds-ratio, risk-difference, risk-ratio, targeted-maximum-likelihood, tmle
- Language: Python
- Homepage: http://zepid.readthedocs.org
- Size: 10.9 MB
- Stars: 148
- Watchers: 9
- Forks: 32
- Open Issues: 48
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-causalinference - zEpid - Epidemiology analysis package including effect measure plots and causal inference tools. (Toolboxes - Environment)
README
# zEpid
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zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The
purpose of this library is to provide a toolset to make epidemiology e-z. A variety of calculations and plots can be
generated through various functions. For a sample walkthrough of what this library is capable of, please look at the
tutorials available at https://github.com/pzivich/Python-for-Epidemiologists
A few highlights: basic epidemiology calculations, easily create functional form assessment plots,
easily create effect measure plots, and causal inference tools. Implemented estimators include; inverse
probability of treatment weights, inverse probability of censoring weights, inverse probabilitiy of missing weights,
augmented inverse probability of treatment weights, time-fixed g-formula, Monte Carlo g-formula, Iterative conditional
g-formula, and targeted maximum likelihood (TMLE). Additionally, generalizability/transportability tools are available
including; inverse probability of sampling weights, g-transport formula, and doubly robust
generalizability/transportability formulas.
If you have any requests for items to be included, please contact me and I will work on adding any requested features.
You can contact me either through GitHub (https://github.com/pzivich), email (gmail: zepidpy), or twitter (@zepidpy).
# Installation
## Installing:
You can install zEpid using `pip install zepid`
## Dependencies:
pandas >= 0.18.0, numpy, statsmodels >= 0.7.0, matplotlib >= 2.0, scipy, tabulate
# Module Features
## Measures
Calculate measures directly from a pandas dataframe object. Implemented measures include; risk ratio, risk difference,
odds ratio, incidence rate ratio, incidence rate difference, number needed to treat, sensitivity, specificity,
population attributable fraction, attributable community risk
Measures can be directly calculated from a pandas DataFrame object or using summary data.
Other handy features include; splines, Table 1 generator, interaction contrast, interaction contrast ratio, positive
predictive value, negative predictive value, screening cost analyzer, counternull p-values, convert odds to
proportions, convert proportions to odds
For guided tutorials with Jupyter Notebooks:
https://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynb
## Graphics
Uses matplotlib in the background to generate some useful plots. Implemented plots include; functional form assessment
(with statsmodels output), p-value function plots, spaghetti plot, effect measure plot (forest plot), receiver-operator
curve, dynamic risk plots, and L'Abbe plots
For examples see:
http://zepid.readthedocs.io/en/latest/Graphics.html
## Causal
The causal branch includes various estimators for causal inference with observational data. Details on currently
implemented estimators are below:
### G-Computation Algorithm
Current implementation includes; time-fixed exposure g-formula, Monte Carlo g-formula, and iterative conditional
g-formula
### Inverse Probability Weights
Current implementation includes; IP Treatment W, IP Censoring W, IP Missing W. Diagnostics are also available for IPTW.
IPMW supports monotone missing data
### Augmented Inverse Probability Weights
Current implementation includes the augmented-IPTW estimator described by Funk et al 2011 AJE
### Targeted Maximum Likelihood Estimator
TMLE can be estimated through standard logistic regression model, or through user-input functions. Alternatively, users
can input machine learning algorithms to estimate probabilities. Supported machine learning algorithms include `sklearn`
### Generalizability / Transportability
For generalizing results or transporting to a different target population, several estimators are available. These
include inverse probability of sampling weights, g-transport formula, and doubly robust formulas
Tutorials for the usage of these estimators are available at:
https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/c_causal_inference
#### G-estimation of Structural Nested Mean Models
Single time-point g-estimation of structural nested mean models are supported.
## Sensitivity Analyses
Includes trapezoidal distribution generator, corrected Risk Ratio
Tutorials are available at:
https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/d_sensitivity_analyses