Driving Health Forward: How Target Trial Emulation is Transforming Evidence Generation

The landscape of clinical evidence generation is undergoing a transformative shift. Stakeholders are working toward a future where studies are more patient-centric, data-driven, and methodologically robust. A recent article published in Clinical Pharmacology & Therapeutics, titled Clinical Evidence 2030, outlines a vision for how clinical research can be optimized over the next decade. This vision emphasizes six core principles that aim to improve study efficiency, transparency, and the integration of real-world evidence (RWE) alongside randomized controlled trials (RCTs).

One of the key methodological advancements highlighted in this evolving research paradigm is Target Trial Emulation, an approach advocated for by Adigens Health’s co-founder, Miguel Hernán as a way of asking causal questions when using observational data. His recent post discusses a new publication in Annals of Internal Medicine, where he and colleagues explain why and when the Target Trial framework is most useful.

Aligning Clinical Evidence 2030 with Target Trial Emulation

The Clinical Evidence 2030 article highlights the increasing need for structured study designs that mitigate biases and improve the reliability of clinical findings. The principles outlined in the paper include:

• Patients at the Centre of Clinical Evidence Generation – Ensuring that research questions and study designs are informed by patient needs.

• Leveraging Existing Data and Knowledge – Using prior research, RWE, and regulatory insights to streamline clinical development.

• Evidence Gaps Drive Research Questions – Designing studies based on clearly articulated questions that address key gaps in medical knowledge.

• Expanding Data and Methods for Smarter Trials – Incorporating RWE alongside RCTs to enhance generalizability and efficiency.

• Collaborative and Early Planning of Clinical Evidence – Coordinating evidence generation across stakeholders from the outset.

• Transparency as a Foundation for Trust – Promoting data sharing and collaboration to enhance credibility and trust.

These are powerful recommendations, but without efficient implementation, they risk becoming just another wish list—familiar ideas that fail to drive real change. The challenge isn’t what needs to be done, but how to do it in a way that integrates seamlessly into existing research and regulatory workflows. Among these, the Target Trial Framework stands out as the most practical and immediately actionable approach—offering a structured way to bridge RCTs and RWE while improving causal inference with minimal disruption to current practices.

Looking Ahead: Clarity in Inquiry, Strategy in Execution, Success in Evidence Generation

A major challenge in clinical research is ensuring that observational studies—those using RWE—are designed in a way that minimizes bias and enhances causal inference. This is precisely where Target Trial Emulation plays a crucial role.  Miguel Hernán and colleagues’ latest article in (The Target Trial Framework for Causal Inference from Observational Data: Why and When Is It Helpful?) expands on the importance of using target trial emulation to improve causal inference in observational studies. At high level, key takeaways include:

• A Simple Two-Step Approach: The target trial framework involves (1) specifying the protocol of a hypothetical randomized trial (the target trial) that answers the causal question of interest, and (2) using observational data to emulate that trial. This structure ensures that research questions are precise and study designs are methodologically sound.

• Prevention of Design-Induced Biases: By explicitly defining eligibility criteria, treatment strategies, and follow-up periods, target trial emulation helps avoid biases such as immortal time bias and selection bias, which have historically compromised observational studies.

• Limitations of Observational Data: While target trial emulation improves study design, it does not resolve issues stemming from poor data quality, unmeasured confounding, or measurement errors. Researchers must still assess and address these limitations when interpreting results.

• Clarifying Causal Questions: One of the most impactful contributions of the target trial framework is reducing ambiguity in causal research. By structuring studies in a way that mimics RCTs, researchers ensure that their analyses answer well-defined causal questions.

As we move toward 2030, integrating Target Trial Emulation into clinical research will be essential for making better use of real-world data, enhancing regulatory decision-making, and improving patient outcomes. At Adigens Health, we believe that bridging the gap between RCTs and RWE with robust methodologies will be a cornerstone of future clinical research.

With RCTs facing limitations related to feasibility, cost, and generalizability to broader patient populations, RWE offers valuable insights into treatment effects in real-world settings.  To truly value it as evidence source, rigour is needed when dealing with biases and confounding. By integrating structured frameworks such as Target Trial Emulation, we can harness the strengths of both approaches—preserving the methodological rigor of RCTs while leveraging the scale, diversity, and real-world applicability of RWE. This synergy will enable more precise, actionable, and patient-centred clinical insights, ultimately driving better decision-making for regulators, healthcare providers, and patients. This ability to strategically combine RCTs and RWE will be essential for accelerating innovation, improving treatment strategies, and ensuring equitable healthcare advancements.