Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
Open accessAbstract
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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Causal-inference frameworks, including target-trial emulation, propensity score methods, and instrumental-variable approaches, help separate prognostic associations from treatment effects in observational data.
“Causal-inference frameworks, including target-trial emulation, propensity score methods, and instrumental-variable approaches, help separate prognostic associations from treatment effects in observational data.63”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [63]
The introduction (p. 1) presents target-trial emulation as a framework for estimating treatment effects from observational data. The Assignment procedures section (p. 3) also identifies propensity-score matching as a way to adjust for confounding. The copy does not address instrumental-variable approaches, so it does not support that part of the sentence. (LLM, conf=0.86)
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- Authors
- Hernán, M. A., & Robins, J. M.
- ORCID
- Robins, James M.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-03-18
- Volume
- 183
- Issue
- 8
- Pages
- 758-764
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC4832051
- OpenAlex
- W2298338128
- Cited by
- 3,115
- References
- 58
- Citations by year
- 2026: 856 · 2025: 595 · 2024: 411 · 2023: 352 · 2022: 332 · 2021: 242 · 2020: 154 · 2019: 85 · 2018: 38 · 2017: 37 · 2016: 13
- FWCI
- 59.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health (R01 AI102634, P01 CA134294)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- big data, target trial emulation, observational studies, causal inference, comparative effectiveness research
- MeSH terms
- Breast Neoplasms, Drug Therapy, Combination, Female, Humans, Progestins, Estrogen Replacement Therapy, Causality, Randomized Controlled Trials as Topic, Databases, Factual, Postmenopause, Comparative Effectiveness Research, Propensity Score, Observational Studies as Topic
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