Target Trial Emulation as a Foundation for the Design of Prospective Randomized Trials

Randomized controlled trials (RCTs) remain the gold standard for evaluating treatment efficacy, yet their execution is often constrained by cost, time, and ethical considerations. Target Trial Emulation (TTE) has emerged as a robust approach to generating causal evidence from observational data, mimicking the design of a hypothetical RCT by structuring real-world data to minimize biases. While TTE has traditionally been used to assess treatment effectiveness retrospectively, this paper explores its prospective role in designing randomized trials, particularly Bayesian adaptive trials.TTE provides critical insights into treatment effects, patient subgroups, and trial feasibility, refining trial designs before prospective execution. By estimating real-world treatment effects, dropout probabilities, and outcome distributions, TTE enhances Bayesian adaptive trial simulations, allowing for dynamic modifications such as adaptive randomization, early stopping rules, and dose adjustments. These simulations optimize sample size, ensuring efficient use of resources while maintaining statistical rigor.Key clinical applications include cardiovascular medicine, oncology, perioperative care, and mental health, where TTE has already informed treatment sequencing, intervention effectiveness, and population-specific responses. The integration of TTE with Bayesian methodologies allows researchers to design data-driven, patient-centered trials that balance feasibility with clinical relevance.By positioning Target Trial Emulation as a foundational step in prospective trial design, this paper advocates for a paradigm shift where observational insights inform and refine experimental methodologies, reducing inefficiencies and improving the translation of research findings into clinical practice. This integration strengthens evidence-based medicine, ensuring trials are both scientifically rigorous and aligned with real-world healthcare needs.