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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.
I recently completed a full RWE study on GI bleeding risk with common drugs — NSAIDs, aspirin, and blood thinners. Used OMOP CDM data, built the whole pipeline in R: cohorts, propensity matching, outcome analysis. Found that NSAIDs and antiplatelets have about 7 times higher bleeding risk compared to anticoagulants.
Network meta-analysis R tutorial with AI-assisted prompt engineering for evidence synthesis, systematic review methodology, and comparative effectiveness research