In medical research, a central objective is to determine whether specific risk factors or interventions have a meaningful impact on clinical outcomes or disease progression. The most rigorous approach to answering such causal questions is the randomized controlled trial (RCT), in which participants are prospectively assigned to interventions under controlled conditions.
Evidence Pyramid; from the Oxford Centre for Evidence-Based Medicine
RCTs are considered the gold standard because they:
• Directly address causal questions
• Provide well-defined causal estimands
• Establish a clear time zero (start of follow-up)
• Ensure structured reporting of drop-outs and missing data
• Minimize confounding through randomization
However, RCTs are not always feasible due to ethical, logistical, financial, or time constraints. Consequently, a substantial portion of clinical evidence is derived from observational studies, including cohort studies and registries, which may be collected prospectively or retrospectively.
Association vs. Causality
A critical distinction in observational research needs to be made between:
- Association: A statistical relationship between variables
- Causality: A direct effect of one variable on another, implying generalizability beyond the study population
The latter, causality, is very challenging in observational studies, as these studies lack randomization and are therefore vulnerable to multiple sources of bias, including selection bias, information bias, attrition bias, time bias and confounding (both measured and unmeasured). These biases complicate causal interpretation and limit the strength of conclusions that can be drawn from observational studies.
Target Trial Emulation: Causal inference from observational data?
Target trial emulation is one concept that has been develop to address this problem. The approach aims to approximate (emulate) a pragmatic randomized trial using observational data – thereby enabling causal inference from such studies.
Rather than analyzing observational data in an ad hoc manner, researchers explicitly define the protocol of a hypothetical RCT (the “Target Trial”) and then emulate it using the available data.
Key Steps in Target Trial Emulation
- Define the target trial protocol
Specify all elements of a hypothetical RCT, including:
o Eligibility criteria
o Treatment strategies (interventions and comparators)
o Study procedures
o Start of follow-up (time zero)
o Outcomes of interest
o Statistical analysis plan (pre-specified) - Emulate the trial in observational data
o Identify individuals in the dataset who meet the eligibility criteria
o Ensure data availability across all relevant time points
o Align exposure, follow-up, and outcome definitions with the target trial - Analyze according to the predefined plan
o Perform analyses strictly following the predefined protocol
o Estimate effect measures such as risk ratios (RR) or hazard ratios (HR)
How to specify and emulate a target trial (from Hernán et al. 2016 and Hansford et al. 2023)
This approach aims to reduce selection bias (by clearly defining the study population in advance), minimize time-related biases (e.g., immortal time bias) through explicit definition of time zero, increase transparency and reproducibility (via a protocol-driven design) and enable more causal interpretation compared to standard observational approaches.
However, target trial emulation cannot fully replicate an RCT. For once, target trials will always remain pragmatic trials conducted in real-world conditions, and randomization or blinding cannot be emulated. Therefore, residual confounding, measurement and performance bias remain major concerns, and need to be handled using methods such as multivariable regression or sensitivity analyses (see our Stats Made Simple Episode on bias and confounding). Additionally, the approach depends strongly on data quality; poor or incomplete data can never be fully corrected statistically
Therefore, target trials are well-suited for pragmatic, real-world questions, where high-quality, large-scale observational data are available, but randomized trials are currently not available or impractical.
To summarize, target trial emulation represents an interesting statistical framework for improving causal inference from observational research, and the method is increasingly used in medical research. By explicitly mimicking a randomized trials, it enhances methodological rigor and interpretability of observational studies. However, careful attention to confounding, bias, and data quality remains essential, and findings should be interpreted within these constraints.
REFERENCES
- Hansford HJ, Cashin AG, Jones MD, Swanson SA, Islam N, Dahabreh IJ, et al. Development of the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) guideline. BMJ Open. 2023 Sep 12;13(9):e074626.
- Cashin AG, Hansford HJ, Hernán MA, Swanson SA, Lee H, Jones MD, et al. Transparent Reporting of Observational Studies Emulating a Target Trial-The TARGET Statement. JAMA. 2025 Sep 23;334(12):1084–93.
- Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure-time settings with competing events. Stat Med. 2020 Apr 15;39(8):1199–236.
- 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. 2016 May 27;79:70–5.
- Hernán MA, Wang W, Leaf DE. Target trial emulation: A framework for causal inference from observational data. JAMA. 2022 Dec 27;328(24):2446–7.
- Hernán MA, Dahabreh IJ, Dickerman BA, Swanson SA. The target trial framework for causal inference from observational data: why and when is it helpful? Ann Intern Med. 2025 Mar;178(3):402–7.
Written by Victoria Konzett, EMEUNET Newsletter Sub-Committee member