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.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.
Riggi et al. (Thu,) studied this question.