Target trial emulation: bridging observational studies and randomized trials for health decision-making
Publication: Journal of Comparative Effectiveness Research
Abstract
Randomized controlled trials (RCTs) are the gold standard generating evidence owing to their rigorous methodology. However, their logistical, financial and ethical limitations highlight the need for alternative approaches using real-world data. Target trial emulation (TTE) applies RCT design principles to estimate causal effects when trials are infeasible. TTE involves three steps: formulating a precise causal research question, explicitly specifying the protocol of the target trial, and rigorously replicating each component of the target trial, such as the eligibility criteria, treatment assignment and follow-up period, using available observational data. Statistical methods commonly used include propensity score matching, inverse probability weighting, G-methods and/or instrumental variables to address confounding and align observational data with the target trial design. Nonetheless, residual confounding, missing data and misclassification can bias results. Sensitivity analyses and transparent reporting are recommended. Notably, TTE frameworks utilizing continuously updated registry data enable ‘living protocols’ that can be iteratively refined as new data accumulate, representing an important evolution toward prospective-retrospective hybrid designs that maintain causal clarity while addressing emerging clinical questions. Though valuable, TTE complements rather than replaces RCTs, as both inform causal inference and clinical decisions.
Plain language summary: Using real-world data to answer clinical questions when randomized trials are not possible
What is this article about?
This article describes how researchers can use real-world medical data from registries to design studies that mimic the structure of randomized controlled trials. This approach, called target trial emulation (TTE), applies the same principles as a clinical trial but uses existing health data instead of enrolling new participants. Unlike traditional retrospective analyses that use fixed datasets, registry-based TTE studies can take advantage of continuously updated data, creating opportunities for ‘living protocols’ that evolve as new information becomes available.
What were the results or methods described?
The article highlights how TTE can improve the quality and reliability of registry-based research. By requiring researchers to predefine study criteria and use robust statistical methods to adjust for confounding factors, TTE reduces bias, prevents selective reporting and encourages transparent and reproducible science.
What do the results mean and why is this important?
Registry-based TTE transforms observational research into a structured, causal framework that produces more credible and clinically relevant findings. The ‘living protocol’ approach allows studies to adapt as data accumulate, supporting timely and trustworthy evidence generation. By applying TTE principles, registry research can become a cornerstone of evidence-based medicine while maintaining scientific rigor and public trust.
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References
Papers of special note have been highlighted as: • of interest
1.
Caparrotta TM, Dear JW, Colhoun HM, Webb DJ. Pharmacoepidemiology: using randomised control trials and observational studies in clinical decision-making. Br. J. Clin. Pharmacol. 85(9), 1907–1924 (2019).
2.
Gale RP, Zhang MJ, Lazarus HM. The role of randomized controlled trials, registries, observational databases in evaluating new interventions. Best Pract. Res. Clin. Haematol. 36(4), 101523 (2023).
3.
Hariton E, Locascio JJ. Randomised controlled trials—the gold standard for effectiveness research. BJOG Int. J. Obstet. Gynaecol. 125(13), 1716 (2018).
4.
Alexander LK, Lopes B, Ricchetti-Masterson K, Yeatts KB. ERIC Notebook. Randomized controlled trials (second edition no.10). UNC Gillings School of Global Public Health. https://sph.unc.edu/epid/eric/
5.
Wilkinson J, Heal C, Antoniou GA et al. Assessing the feasibility and impact of clinical trial trustworthiness checks via an application to Cochrane reviews: Stage 2 of the INSPECT-SR project. J. Clin. Epidemiol. 184, 111824 (2025).
6.
Jager KJ, Zoccali C, MacLeod A, Dekker FW. Confounding: what it is and how to deal with it. Kidney Int. 73(3), 256–260 (2008).
7.
Alexander LK, Lopes B, Ricchetti-Masterson K, Yeatts KB. ERIC Notebook. Confounding bias part I (second edition no.11). UNC Gillings School of Global Public Health. https://sph.unc.edu/epid/eric/
8.
Alexander LK, Lopes B, Ricchetti-Masterson K, Yeatts KB. ERIC Notebook. Confounding bias part II (second edition no.12). UNC Gillings School of Global Public Health. https://sph.unc.edu/epid/eric/
9.
Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183(8), 758–764 (2016).
• Seminal paper that introduced the target trial emulation (TTE) framework, outlining how to design observational analyses using the structure and rigor of randomized controlled trials.
10.
Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J. Clin. Epidemiol. 79, 70–75 (2016).
11.
Hemkens LG, Contopoulos-Ioannidis DG, Ioannidis JPA. Routinely collected data and comparative effectiveness evidence: promises and limitations. CMAJ 188(8), E158–E164 (2016).
12.
Wilson BE, Booth CM. Real-world data: bridging the gap between clinical trials and practice. eClinicalMedicine 78, 102915 (2024).
13.
Rosenbaum PR. Observational studies. Springer, New York, NY (2002).1–17
14.
Alexander LK, Lopes B, Ricchetti-Masterson K, Yeatts KB. ERIC Notebook. Selection bias (second edition no.13). UNC Gillings School of Global Public Health. https://sph.unc.edu/epid/eric/
15.
Lévesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ 340, b5087 (2010).
16.
Yadav K, Lewis RJ. Immortal time bias in observational studies. JAMA 325(7), 686–687 (2021).
17.
Mamdani M, Rochon P, Juurlink DN et al. Effect of selective cyclooxygenase-2 inhibitors and naproxen on short-term risk of acute myocardial infarction in the elderly. Arch. Intern. Med. 163(4), 481–486 (2003).
18.
Delitto A. Pragmatic clinical trials: implementation opportunity, or just another fad? Phys. Ther. 96(2), 137–138 (2016).
19.
Seewald NJ, McGinty EE, Stuart EA. Target trial emulation for evaluating health policy. Ann. Intern. Med. 177(11), 1530–1538 (2024).
• Demonstrates the application of TTE beyond clinical trials, showing its utility for assessing real-world policy interventions and public health outcomes.
20.
Holland PW. Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945–960 (1986).
21.
Hernán MA, Robins JM. Causal inference: What if. CRC Press, Boca Raton, FL (2020).
22.
Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66(5), 688–701 (1974).
23.
Holland PW. Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945–960 (1986).
24.
Rubin DB. Randomization analysis of experimental data: the Fisher randomization test comment. J. Am. Stat. Assoc. 75(371), 591–593 (1980).
25.
Hernán MA. Methods of public health research—strengthening causal inference from observational data. N. Engl. J. Med. 385(15), 1345–1348 (2021).
26.
Hernán MA, Wang W, Leaf DE. Target trial emulation: a framework for causal inference from observational data. JAMA 328(24), 2446–2447 (2022).
27.
Hernán MA. The C-word: scientific euphemisms do not improve causal inference from observational data. Am. J. Public Health 108(5), 616–619 (2018).
28.
Fu EL. Target trial emulation to improve causal inference from observational data: what, why, and how? J. Am. Soc. Nephrol. 34(8), 1221–1228 (2023).
29.
Petersen ML, van der Laan MJ. Causal models and learning from data. Epidemiology 25(3), 418–426 (2014).
30.
Simon-Tillaux N, Martin GL, Hajage D et al. Conducting observational analyses with the target trial emulation approach: a methodological systematic review. BMJ Open 14(11), e086595 (2024).
• Provides a comprehensive systematic review of TTE applications, documenting methodological trends, reporting quality and future research needs.
31.
Hansford HJ, Cashin AG, Jones MD et al. Reporting of observational studies explicitly aiming to emulate randomized trials: a systematic review. JAMA Netw. Open 6(9), e2336023 (2023).
32.
Nguyen VG, Lewis KM, Gilbert R, Dearden L, De Stavola B. Early special educational needs provision and its impact on unplanned hospital utilisation and school absences in children with isolated cleft lip and/or palate. NIHR Open Res. 3, 54 (2023).
33.
Moler-Zapata S, Hutchings A, O'Neill S, Silverwood RJ, Grieve R. Emulating target trials with real-world data to inform health technology assessment: findings and lessons from an application to emergency surgery. Value Health 26(8), 1164–1174 (2023).
• Illustrates a practical example of TTE applied to real-world health technology assessment, bridging research and regulatory decision-making.
34.
Pearl J. Causal diagrams for empirical research. Biometrika 82(4), 669–688 (1995).
35.
Zivich PN, Cole SR, Westreich D. Positivity: identifiability and estimability. arXiv. https://arxiv.org/abs/2207.05010 (2022).
36.
Cole SR, Frangakis CE. The consistency statement in causal inference: a definition or an assumption? Epidemiology 20(1), 3–9 (2009).
37.
Anders H. Sequence announcement: applied causal inference (online) (2014).
38.
Ho DE, Imai K, King G, Stuart EA. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42(8), 1–28 (2011).
39.
van der Wal WM, Geskus RB. ipw: an R package for inverse probability weighting. J. Stat. Softw. 43(13), 1–23 (2011).
40.
Lin V, McGrath S, Zhang Z. gfoRmula: parametric g-formula. R Package Version 1.1.0 (2024). https://CRAN.R-project.org/package=gfoRmula
41.
VanderWeele TJ, Ding P. Sensitivity analysis in observational research: Introducing the E-value. Ann. Intern. Med. 167(4), 268–274 (2017).
42.
Hansford HJ, Cashin AG, Jones MD et al. Development of the TrAnsparent ReportinG of observational studies emulating a target trial (TARGET) guideline. BMJ Open 13(9), e074626 (2023).
43.
Cashin AG, Hansford HJ, Hernán MA et al. Transparent reporting of observational studies emulating a target trial: The TARGET statement. BMJ 390, e087179 (2025).
• Introduces the TARGET reporting guideline, establishing best practices for transparent and reproducible TTE research.
44.
Robins J. A new approach to causal inference in mortality studies with a sustained exposure period. Math. Model. 7(9–12), 1393–1512 (1986).
45.
Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial. Biometrics 56(3), 779–788 (2000).
46.
Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology 17(4), 360–372 (2006).
47.
Venkataramani AS, Bor J, Jena AB. Regression discontinuity designs in healthcare research. BMJ 352, i1216 (2016).
48.
Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions. Int. J. Epidemiol. 46(1), 348–355 (2017).
49.
Zuo H, Yu L, Campbell SM, Yamamoto SS, Yuan Y. The implementation of target trial emulation for causal inference: a scoping review. J. Clin. Epidemiol. 162, 29–37 (2023).
50.
Scola G, Chis Ster A, Bean D, Pareek N, Emsley R, Landau S. Implementation of the trial emulation approach in medical research. BMC Med. Res. Methodol. 23(1), 186 (2023).
51.
Wang SV, Schneeweiss S, Franklin JM et al. Emulation of randomized clinical trials with nonrandomized database analyses. JAMA 329(16), 1376–1385 (2023).
52.
Groenwold RHH, Sterne JAC, Lawlor DA, Moons KGM, Hoes AW, Tilling K. Sensitivity analysis for the effects of multiple unmeasured confounders. Ann. Epidemiol. 26(9), 605–611 (2016).
53.
Chung WT, Chung KC. The use of the E-value for sensitivity analysis. J. Clin. Epidemiol. 163, 92–94 (2023).
54.
Li P, Stuart EA, Allison DB. Multiple imputation: a flexible tool for handling missing data. JAMA 314(18), 1966–1967 (2015).
55.
Pearce N, Vandenbroucke JP. Are target trial emulations the gold standard for observational studies? Epidemiology 34(5), 614–621 (2023).
56.
US Food and Drug Administration. Real-world data: assessing registries to support regulatory decision-making for drug and biological products. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-registries-support-regulatory-decision-making-drug-and-biological-products
57.
HMA-EMA roadmap advances EU regulatory guidance on real-world evidence. https://becarispublishing.com/digital-content/blog-post/hma-ema-roadmap-advances-eu-regulatory-guidance-real-world-evidence
58.
European Medicines Agency. Reflection paper on use of real-world data in non-interventional studies. https://www.ema.europa.eu/en/reflection-paper-use-real-world-data-non-interventional-studies-generate-real-world-evidence-scientific-guideline
59.
Suchak T, Aliu AE, Harrison C, Zwiggelaar R, Geifman N, Spick M. Explosion of formulaic research articles, including inappropriate study designs and false discoveries, based on the NHANES US National Health Database. PLoS Biol. 23(5), e3003152 (2025).
• Warns of the growing crisis of low-quality, data-mined research; underscores the need for structured causal frameworks like TTE to preserve research integrity.
60.
Spick M, Onoja A, Harrison C, Stender S, Byrne J, Geifman N. Quantifying new threats to health and biomedical literature integrity from rapidly scaled publications and problematic research. MedRxiv. Preprint posted online 9 July 2025. https://www.medrxiv.org/content/10.1101/2025.07.07.25331008v1
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© 2026 The authors. This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License
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Received: 28 October 2025
Accepted: 13 February 2026
Published online: 19 March 2026
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Target trial emulation: bridging observational studies and randomized trials for health decision-making. (2026) Journal of Comparative Effectiveness Research. DOI: 10.57264/cer-2025-0180
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