Package: zepid
zEpidzEpid 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 athttps://github.com/pzivich/Python-for-EpidemiologistsA 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).InstallationInstalling:You can install zEpid usingpip install zepidDependencies:pandas >= 0.18.0, numpy, statsmodels >= 0.7.0, matplotlib >= 2.0, scipy, tabulateModule FeaturesMeasuresCalculate 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 riskMeasures 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 oddsFor guided tutorials with Jupyter Notebooks:https://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynbGraphicsUses 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 plotsFor examples see:http://zepid.readthedocs.io/en/latest/Graphics.htmlCausalThe causal branch includes various estimators for causal inference with observational data. Details on currently
implemented estimators are below:G-Computation AlgorithmCurrent implementation includes; time-fixed exposure g-formula, Monte Carlo g-formula, and iterative conditional
g-formulaInverse Probability WeightsCurrent implementation includes; IP Treatment W, IP Censoring W, IP Missing W. Diagnostics are also available for IPTW.
IPMW supports monotone missing dataAugmented Inverse Probability WeightsCurrent implementation includes the augmented-IPTW estimator described by Funk et al 2011 AJETargeted Maximum Likelihood EstimatorTMLE 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 includesklearnGeneralizability / TransportabilityFor 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 formulasTutorials for the usage of these estimators are available at:https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/c_causal_inferenceG-estimation of Structural Nested Mean ModelsSingle time-point g-estimation of structural nested mean models are supported.Sensitivity AnalysesIncludes trapezoidal distribution generator, corrected Risk RatioTutorials are available at:https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/d_sensitivity_analyses