Estimating Individualized Treatment Rules Without Individual Data in Multicentre Studies

Explore a comprehensive lecture on estimating individualized treatment rules in multicentre studies without accessing individual data. Delve into the challenges of treatment effect heterogeneity and the importance of large datasets in healthcare research. Learn about distributed regression techniques combined with dynamic weighted regression for optimal individualized treatment rule estimation while maintaining data privacy. Examine the robustness and flexibility of this approach through simulations addressing local treatment practices. Gain insights from a real-world analysis of the U.K.'s Clinical Practice Research Datalink on depression treatment. Cover topics including evidence-based medicine, precision medicine, adaptive treatment strategies, analytic methods, data pooling, and distributed regression. Conclude with a discussion on warfarin dosing strategy and key takeaways from this innovative statistical framework.

Intro
Road-map
Evidence-based medicine: The statisticians' role
Precision medicine
When would we want treatment to be adaptive?
Analytic methods
Identifying the best treatment regime
Dynamic Weighted OLS (dWOLS)
Data pooling
Pooling strategies
Distributed regression
Simulation scenarios
Simulation results
Findings
Simulations take 2
Warfarin dosing strategy
Concluding remarks