ehsanx/TMLEworkshop
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.
This project provides an R guide to Targeted Maximum Likelihood Estimation (TMLE) for researchers conducting comparative effectiveness studies. It takes real epidemiological data and demonstrates how to apply TMLE, G-computation, and IPW methods in R, offering a practical, code-first approach. Medical researchers, epidemiologists, and public health scientists who analyze observational data to understand treatment effects would use this.
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Use this if you are a medical researcher or epidemiologist looking for a practical, code-first introduction to applying TMLE and related methods in R for comparative effectiveness studies.
Not ideal if you are seeking a deep theoretical understanding of TMLE or prefer using simulated data to learn statistical concepts rather than real-world examples.
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Jan 05, 2023
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