zepid 0.9.1
Tool package for epidemiologic analyses
License: MIT
Keywords: epidemiology inverse-probability-weights risk-ratio g-computation g-formula IPW AIPW TMLE

![zepid](docs/images/zepid_logo.png)
# 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