A Practical Guide to Target Trial Emulation: Connecting Randomized Trials and Real-World Data in Cardiovascular...

Randomized controlled trials (RCTs) remain the gold standard for causal inference in cardiovascular prevention but are often limited by cost, feasibility, and restricted generalizability. The rapid expansion of real-world data (RWD) offers new opportunities to address clinically relevant questions beyond the scope of RCTs, yet observational analyses remain highly susceptible to bias, particularly when study design is not aligned with the underlying causal question. Target trial emulation (TTE) is an increasingly adopted framework that improves the validity and interpretability of observational studies by explicitly specifying the protocol of the hypothetical randomized trial that would ideally answer the clinical question. This review provides a practical guide to TTE in cardiovascular prevention. We describe its conceptual foundations within the counterfactual framework, emphasizing the shift from a model-driven to a design-first approach, and outline the seven key components of the target trial protocol: eligibility criteria, treatment strategies, assignment procedures, time zero definition, outcomes, estimand, and statistical analysis plan. We clarify the role of analytical methods within TTE, including propensity score approaches, g-computation, and g-estimation, and provide guidance on selecting the appropriate method based on the estimand and treatment strategy. A step-by-step implementation framework is proposed, covering common pitfalls such as immortal time bias and prevalent user bias, the use of negative control analyses as diagnostic tools, and the handling of missing data. Illustrative examples from cardiovascular prevention demonstrate how TTE enhances causal interpretation across a range of clinical questions. TTE strengthens the credibility of real-world evidence by improving transparency, reducing avoidable design biases, and aligning analyses with clinically meaningful decisions. It does not eliminate residual confounding and should be viewed as complementary to, rather than a substitute for, randomized evidence.