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

Am J Epidemiol. 2016 Apr 15;183(8):758-64. doi: 10.1093/aje/kwv254. Epub 2016 Mar 18.


Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a 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. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big 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.

Keywords: big data; causal inference; comparative effectiveness research; target trial.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Breast Neoplasms / epidemiology*
  • Breast Neoplasms / etiology
  • Causality
  • Comparative Effectiveness Research / methods
  • Comparative Effectiveness Research / standards*
  • Databases, Factual / statistics & numerical data
  • Drug Therapy, Combination
  • Estrogen Replacement Therapy / adverse effects
  • Estrogen Replacement Therapy / statistics & numerical data*
  • Female
  • Humans
  • Observational Studies as Topic / methods
  • Observational Studies as Topic / standards*
  • Postmenopause
  • Progestins / adverse effects
  • Progestins / therapeutic use*
  • Propensity Score
  • Randomized Controlled Trials as Topic / methods
  • Randomized Controlled Trials as Topic / standards


  • Progestins