Targeted Learning for the Estimation of Drug Safety and Effectiveness: Getting Better Answers by Asking Better Questions

Targeted Learning for the Estimation of Drug Safety and Effectiveness: Getting Better Answers by Asking Better Questions

Summary

In this workshop, Dr. Schnitzer gives an overview of the estimation of causal effects using Targeted Learning and how this approach can be applied in drug safety and effectiveness research.
Targeted Learning (TL) focuses on the clear delineation of assumptions, definition of the parameter of interest, usage of high-performance statistical methods to target estimation of the parameter of interest, and careful interpretation of the resulting estimate. The estimation step involves the usage of Targeted Minimum Loss-based Estimation (van der Laan and Rubin, 2006) and an ensemble learning method known as Super Learner (van der Laan, Pulley and Hubbard, 2007).
This talk will qualitatively contrast traditional statistical modeling (e.g. model fitting) and flexible prediction methods (e.g. machine learning) with the TL approach. Special focus will be placed on estimation in high-dimensional covariate spaces and variable selection.