Git Product home page Git Product logo

ehsanx / tmleworkshop Goto Github PK

View Code? Open in Web Editor NEW
8.0 3.0 2.0 22.34 MB

Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.

Home Page: https://ehsanx.github.io/TMLEworkshop/

License: Creative Commons Zero v1.0 Universal

Dockerfile 0.01% Shell 0.06% TeX 24.83% HTML 67.37% CSS 4.67% JavaScript 2.77% R 0.30%
tmle machine-learning double robust r workshop-materials g-computation propensity-score comparative-effectiveness ipw

tmleworkshop's Introduction

R Guide for TMLE in Medical Research

Background

In comparative effectiveness studies, researchers typically use propensity score methods. However, propensity score methods have known limitations in real-world scenarios, when the true data generating mechanism is unknown. Targeted maximum likelihood estimation (TMLE) is an alternative estimation method with a number of desirable statistical properties. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified. TMLE also enables the integration of machine learning approaches. Despite the fact that this method has been shown to perform better than propensity score methods in a variety of scenarios, it is not widely used in medical research as the technical details of this approach are generally not well understood.

Goal

In this workshop we will present an introductory tutorial explaining an overview of

  • TMLE and
  • some of the relevant methods
    • G-computation and
    • IPW

using one real epidemiological data,

  • the steps to use the methods in R, and
  • a demonstration of relevant R packages.ย 

Philosophy

Code-first philosophy is adopted for this workshop; demonstrating the analyses through one real data analysis problem used in the literature.

  • This workshop is not theory-focused, nor utilizes simulated data to explain the ideas. Given the focus on implementation, theory is beyond the scope of this workshop.
  • At the end of the workshop, we will provide key references where the theories are well explained.

Pre-requisites

  • Basic understanding of R language is required.
  • A general understanding of multiple regression is expected.
  • Familiarity with machine learning and epidemiological core concepts would be helpful, but not required.
  • Deep understanding of causal inference or advanced statistical inference knowledge is not expected.

Version history

The workshop was first developed for R/Medicine Virtual Conference 2021, August 24th; title: An Introductory R Guide for Targeted Maximum Likelihood Estimation in Medical Research.

Contributor list

Hanna Frank (SPPH, UBC) Ehsan Karim (SPPH, UBC)

License

The online version of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You may share, adapt the content and may distribute your contributions under the same license (CC BY-NC-SA 4.0), but you have to give appropriate credit, and cannot use material for the commercial purposes.

How to cite

Karim, ME and Frank, H (2021) "R Guide for TMLE in Medical Research", URL: ehsanx.github.io/TMLEworkshop/, (v1.1). Zenodo. https://doi.org/10.5281/zenodo.5246085

tmleworkshop's People

Contributors

ehsanx avatar hantonita avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.