Using Big Data to Emulate a Target Trial When a Randomized Trial is Not Available.

Ideally, questions about comparative effectiveness or safety would be answered by an appropriately designed and conducted randomized experiment. When we cannot conduct the randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. This talk outlines a framework for comparative effectiveness research using observational data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls