Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

Overview

Python-for-Epidemiologists

Join the chat at https://gitter.im/zEpid/community DOI

This repository is an introduction to epidemiology analyses in Python. Additionally, the tutorials for my library zEpid are hosted here. For more information on zEpid, see GitHub or ReadTheDocs.

The directory of this guide is

  1. Python Basics
  2. Basics of pandas (data management library)
  3. Epidemiology analyses in Python
    1. Basics
    2. Missing data
    3. Causal inference
      1. Time-fixed treatments
      2. Time-varying treatments
    4. Sensitivity analyses

Required packages for tutorial

To complete the tutorial, user must have the following packages installed: numpy, pandas, zepid, matplotlib, statsmodels, lifelines, and sklearn

IDE (Integrated Development Environment)

No IDE is required to complete the tutorial. All files are available in ipynb also known as jupyter notebooks. Code can be either downloaded or copied from the notebooks.

Here are some IDEs I have used in the past (and what I believe to be their advantages and disadvantages

Rodeo

This is the IDE I used for a long time. It is set up like RStudio

Advantages:

Basically RStudio but for Python, decent interface, easy to run line-by-line, easy to visualize plots (although it encourage bad habits)

Disadvantages:

Does not have all the features of RStudio (will delete changes if closed without saving), sucks up a lot of memory, sometimes the auto-complete would stop working if I hit more than 300+ lines of code, the environment tab is not great (don't expect it to open anything like RStudio)

Aside: their website has great tutorials how to run some basic stuff in Python if you are new to analysis in Python https://rodeo.yhat.com/

jupyter notebooks

Designed to be like a lab notebook, or like R markdown. Supports a pseudo-line-by-line concept Good for writing, since it allows for MarkDown. While I know a lot of people like jupyter, I only really use it for examples of code, not my personal programming. I never liked how it had to open via a Web Browser. I would rather have it be separate program. However, all guides were made using this IDE

PyCharm

This is the IDE I currently use

Advantages:

Easily set up virtual environments, interacts natively with Git, supports different file formats with plug-ins (e.g. .md), enforces certain coding conventions, better debug code features, organization of files under the project tab are convenient

Disadvantages:

Not great for running line-by-line code (it can do it, just not as elegantly), little more hardcore (I wouldn't really consider it a beginner's IDE. It requires some knowledge of set-up of Python)

IDLE

Ships with the basic Python 3.x installation. It is very basic and does not support line-by-line. Wouldn't recommend unless you are just starting with Python and don't want to commit to an IDE yet

Spyder

Ships with conda. Not bad but I didn't use it that much (I couldn't get the hang of it). Similarly it is an RStudio copy. Can't say too much since I haven't used it extensively

Basic Introduction to Python

If you have never used Python before, I have created some introductory materials to Python and the data management library I use, pandas. These are basic guides, but they also point to other resources. Please READ ALL OF THE BELOW BEFORE PROCEEDING.

Installing Python

To install, Python 3.x, we can download it directly from: https://www.python.org/downloads/

The installer provides an option to add Python3 to PATH, it is highly recommended you do this, since it allows you to avoid having to do it manually

Open Command Prompt / Terminal. When opened, type python and this should open Python in the same window. From here, you can quit by typing 'quit()' or closing the window. If this does NOT work, make sure your environmental variable was created properly

Installing Python Packages

Packages are what stores Python functions that we will use. These packages are contributed by various members of the community (including me)) and there is a wide array. To be able to download packages, we need to make sure we have an environmental variable created for python. We will discuss how to install packages

Python 3.x conveniently comes with a package manager. Basically it stores all the packages and we can use it to download new ones or update already downloaded ones.

To download a new package: Open Command Prompt/Terminal and use the following code (we will be installing pandas)

pip install pandas

To update a Python package, type the following command into Command Prompt. For example, we will update our pandas package

pip install pandas --upgrade

That concludes the basics. Please review parts 1 and 2 of the tutorials next

Comments
  • Cochran-Mantel-Haenszel

    Cochran-Mantel-Haenszel

    Thank you @pzivich for this amazing resource. Having the Hernan/Robbins causal model code in python is super helpful... g-estimation!

    I have a request... do you have a Cochran-Mantel-Haenszel script? If you get the chance, please, it would be useful to us to have in this repo. Thank you in advance!

    opened by opioiddatalab 2
  • Slight changes in Incidence Rate Ratio

    Slight changes in Incidence Rate Ratio

    Incidence Ratio Rate Paragraph

    • Fixed repetition
    • T1 & T0 are defined the same way. I believe that T0 is the person-time contributed by people NOT treated with ART
    opened by jaimiles23 0
  • Updates for v0.8.0

    Updates for v0.8.0

    Checklist for various notebooks to update with v0.8.0 release (hasn't released yet)

    • [x] IPTW update. Lots of major changes, so notebook needs to be completely overhauled

    • [x] Demonstrate new diagnostic functions for IPTW, g-formula, AIPW, TMLE

    • [x] Demonstrate g-bound argument

    • [x] Remove TMLE machine learning custom models. This is being removed in favor of cross-fitting. Can leave how to apply for now, but add the warning and mention will be cut in v0.9.0

    opened by pzivich 0
  • Notebooks not rendering in GitHub

    Notebooks not rendering in GitHub

    Sometimes GitHub has trouble rendering the notebooks. AFAIK the rendering system is behind the scenes at GitHub. Others have this same problem across repos and it sometimes occurs to me as well.

    If the notebook won't render in GitHub, you can copy the URL to the notebook you want to view and use the following site to view the notebook: https://nbviewer.jupyter.org/

    opened by pzivich 0
  • Replicate

    Replicate "Causal Inference"

    Issue to track progress on implementation of Hernan and Robins "Causal Inference" chapters

    • [x] Chapter 12: Inverse probability weights

    • [x] Chapter 13: Parametric g-formula

    • [x] Chapter 14: G-estimation of structural nested models

    • [x] ~Chapter 16: G-estimation for IV analysis~

    • [ ] Chapter 17: Causal survival analysis

    • [ ] Part III: Time-varying treatments

    ~G-estimation is not currently implemented. I will need to implement these before chapter 14 can be done.~

    Currently there are no plans to replicate Chapter 15 (propensity scores and regression) or Chapter 16 (instrumental variables) since the first method does not require zEpid and I am unfamiliar with the second. Maybe instrumental variables will be added in the future?

    For Chapter 16, I am considering demonstrating the usage of g-estimation instead of two-stage least-squares. Specifically, using the same data as done in Chapter 16 but following Technical Point 16.3

    enhancement 
    opened by pzivich 0
  • Tutorials

    Tutorials

    On the website, create quick tutorials demonstrating each of the implemented estimators, descriptions of how they work, and why you might want to use them. Might be more digestible than the current docs (also better justify why to choose one over the other)

    Reference to base on https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html https://github.com/CamDavidsonPilon/lifelines/blob/master/docs/jupyter_notebooks/Proportional%20hazard%20assumption.ipynb

    TODO

    • [x] Basic measures

    • [x] splines

    • [x] IPTW: time-fixed treatment

    • [ ] IPTW: stochastic treatment

    • [ ] IPTW: time-varying treatment

    • [x] IPCW

    • [x] IPMW: single variable

    • [ ] IPMW: monotone

    • [ ] IPMW: nonmonotone (to add after implemented)

    • [x] G-formula: time-fixed binary treatment, binary outcome

    • [x] G-formula: time-fixed categorical treatment, binary outcome

    • [ ] G-formula: time-fixed continuous treatment, binary outcome (to add after implemented)

    • [x] G-formula: time-fixed binary treatment, continuous outcome

    • [x] G-formula: Monte Carlo

    • [x] G-formula: Iterative Conditional

    • [x] G-estimation of SNM

    • [x] AIPTW

    • [ ] AIPMW

    • [x] TMLE

    • [x] TMLE: stochastic treatment

    • [ ] LTMLE (to add after implemented)

    • [x] Quantitative bias analysis

    • [x] Functional form assessment

    • [x] Generalizability

    • [ ] Transportability (IPSW, g-transport, AIPSW)

    • [x] Monte Carlo g-formula by-hand (helps to explain underlying process)

    opened by pzivich 1
Releases(v0.8.0)
Owner
Paul Zivich
Epidemiology post-doc working in epidemiologic methods and infectious diseases.
Paul Zivich
Location-Sensitive Visual Recognition with Cross-IOU Loss

The trained models are temporarily unavailable, but you can train the code using reasonable computational resource. Location-Sensitive Visual Recognit

Kaiwen Duan 146 Dec 25, 2022
Image-generation-baseline - MUGE Text To Image Generation Baseline

MUGE Text To Image Generation Baseline Requirements and Installation More detail

23 Oct 17, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation This is the implementation of the approach describ

Taosha Fan 47 Nov 15, 2022
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

231 Jan 05, 2023
Joint detection and tracking model named DEFT, or ``Detection Embeddings for Tracking.

DEFT: Detection Embeddings for Tracking DEFT: Detection Embeddings for Tracking, Mohamed Chaabane, Peter Zhang, J. Ross Beveridge, Stephen O'Hara

Mohamed Chaabane 253 Dec 18, 2022
This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks

NNProject - DeepMask This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks. Th

189 Nov 16, 2022
Lucid library adapted for PyTorch

Lucent PyTorch + Lucid = Lucent The wonderful Lucid library adapted for the wonderful PyTorch! Lucent is not affiliated with Lucid or OpenAI's Clarity

Lim Swee Kiat 520 Dec 26, 2022
Model of an AI powered sign language interpreter.

TEXT AND SPEECH TO SIGN LANGUAGE. A web application which takes in text or live audio speech recording as input, converts and displays the relevant Si

Mark Gatere 4 Mar 30, 2022
Code for the paper "How Attentive are Graph Attention Networks?"

How Attentive are Graph Attention Networks? This repository is the official implementation of How Attentive are Graph Attention Networks?. The PyTorch

175 Dec 29, 2022
The audio-video synchronization of MKV Container Format is exploited to achieve data hiding

The audio-video synchronization of MKV Container Format is exploited to achieve data hiding, where the hidden data can be utilized for various management purposes, including hyper-linking, annotation

Maxim Zaika 1 Nov 17, 2021
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Alexander Amini 75 Dec 15, 2022
This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".

Deep Conditional Gaussian Mixture Model for Constrained Clustering. This repository holds the code for the paper Deep Conditional Gaussian Mixture Mod

17 Oct 30, 2022
Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

DFN:Distributed Feedback Network for Single-Image Deraining Abstract Recently, deep convolutional neural networks have achieved great success for sing

6 Nov 05, 2022
GrabGpu_py: a scripts for grab gpu when gpu is free

GrabGpu_py a scripts for grab gpu when gpu is free. WaitCondition: gpu_memory

tianyuluan 3 Jun 18, 2022
Tree-based Search Graph for Approximate Nearest Neighbor Search

TBSG: Tree-based Search Graph for Approximate Nearest Neighbor Search. TBSG is a graph-based algorithm for ANNS based on Cover Tree, which is also an

Fanxbin 2 Dec 27, 2022
PyTorch implementation of a collections of scalable Video Transformer Benchmarks.

PyTorch implementation of Video Transformer Benchmarks This repository is mainly built upon Pytorch and Pytorch-Lightning. We wish to maintain a colle

Xin Ma 156 Jan 08, 2023
Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

FPT_data_centric_competition - Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

Pham Viet Hoang (Harry) 2 Oct 30, 2022
Official implementation of MSR-GCN (ICCV 2021 paper)

MSR-GCN Official implementation of MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction (ICCV 2021 paper) [Paper] [Sup

LevonDang 42 Nov 07, 2022