R Guide for TMLE in Medical Research
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 implementation details of this approach are generally not well understood.
In this workshop we will present an introductory tutorial explaining an overview of
- TMLE and
- some of the relevant methods
- G-computation and
using one real epidemiological data,
- the steps to use the methods in R, and
- a demonstration of relevant R packages.
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.
- 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.
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’.
Feel free to reach out for any comments, corrections, suggestions.
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