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 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 vie...

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Vydáno v:American journal of epidemiology Ročník 183; číslo 8; s. 758
Hlavní autoři: Hernán, Miguel A, Robins, James M
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 15.04.2016
Témata:
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
comparative effectiveness research
big data
causal inference
target trial
ISSN:1476-6256, 1476-6256
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Shrnutí: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.
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ISSN:1476-6256
1476-6256
DOI:10.1093/aje/kwv254