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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| Published in: | American journal of epidemiology Vol. 183; no. 8; p. 758 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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United States
15.04.2016
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| ISSN: | 1476-6256, 1476-6256 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Hernán, Miguel A Robins, James M |
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| Copyright | The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. |
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| Keywords | comparative effectiveness research big data causal inference target trial |
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| SubjectTerms | 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 |
| Title | Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available |
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