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.

Example code for the handbook "Comparative effectiveness and personalized medicine using real-world data"

Network meta-analysis R tutorial with AI-assisted prompt engineering for evidence synthesis, systematic review methodology, and comparative effectiveness research