At its core, an estimand defines the treatment effect a clinical trial is designed to estimate — in other words, the precise question the study is trying to answer. By explicitly stating what is being compared, in whom, on what endpoint, and under which circumstances, estimands bring clarity to trial objectives and ensure that all stakeholders are aligned from the outset.
This clarity matters. Clinical trials often generate ambiguity when protocol design, data collection, analysis, and interpretation are not fully aligned. Estimands address this by creating a shared framework that connects the clinical question to the statistical analysis, helping teams avoid misinterpretation and increasing confidence in trial results.
Recognizing this need, the International Council for Harmonisation (ICH) E9(R1) guideline on statistical principles for clinical trials formalized the estimand framework, giving the industry a common language to discuss key aspects of trial design. While the individual components of an estimand are not new, the requirement to define them explicitly at the protocol stage has fundamentally changed how sponsors, statisticians, clinicians, and regulators interact and collaborate. Used effectively, the framework aligns teams around a shared understanding of what a trial is truly designed to answer, while supporting sponsors in trial planning and regulators in their review.
An estimand comprises five key elements: the treatment condition, the population, the endpoint, intercurrent events, and the population-level summary. Among these, intercurrent events — events occurring after treatment initiation that affect either the interpretation or the existence of the measurements relevant to the clinical question — are often the most impactful. Defining these events early is critical to selecting appropriate handling strategies, mitigating their risk, clarifying which data must be collected and monitored, and ensuring that trial results are interpreted as intended. This structured approach strengthens protocol design, enables more targeted monitoring, and ultimately leads to more reliable and interpretable data.
To fully realize the value of estimands, their implementation should be guided by three core principles: simplicity, collaboration, and education. Together, these principles help ensure that trials are not only scientifically rigorous but also practical, transparent, and regulator-ready.
Keeping it simple
Estimands offer a powerful tool for refining trial design, but one of the most common mistakes in estimand implementation is unnecessary complexity. It can be tempting to build highly detailed strategies for every possible scenario, but overly intricate definitions often make protocols difficult to interpret and even harder to implement. The goal is not to account for every exception, but to define a clear, interpretable approach that reflects the trial’s objective, increases the reliability of data collected, and can be easily understood by everyone involved. Simplicity ensures that estimands remain a practical, rather than theoretical, contribution to study design.
Building this simplicity comes from shared insights from across the study team; biostatisticians cannot, and should not, determine in isolation which intercurrent events are relevant, or how they should be addressed. Medical monitors, clinicians, and operational colleagues can bring essential insights into real-world scenarios that may affect trial outcomes. This is as important for safety-focused trials (Phases 1 and 2a) as for efficacy-focused trials (Phases 2b and 3), or when working with compounds known to have serious adverse effects. Early collaboration across functions enables better definitions, more feasible strategies and improved study integrity.
Building understanding through education
A key barrier to collaboration and wider adoption of estimands is a lack of familiarity. The terminology can be off-putting, and they may appear overly technical or burdensome. Education plays a critical role in addressing this. Improving estimand literacy across teams not only supports planning and scientific integrity, but also regulatory compliance. When all colleagues fully understand each component, they are better equipped to challenge assumptions, identify risks, and contribute meaningfully to protocol development. This shared understanding leads to more robust study designs and reduces the risk of costly amendments later in the process.
For sponsors without in-house biostatistics or regulatory expertise, outsourcing to an experienced partner can also help to bridge this gap. Collaborating with a clinical research organization (CRO) familiar with estimand planning not only eases the technical burden, but brings additional insight to trial design and execution. When stakeholders understand that estimands are about clearly defining the study objective and increasing the reliability of the collected data, not merely ticking a regulatory box, they are more likely to recognize their value. This, in turn, fosters stronger early planning and alignment between sponsors, CROs and regulators.
Risk planning and data quality
Well-defined estimands can benefit the whole study, by ensuring operational readiness from the outset. This supports the development of tailored, risk-based monitoring strategies that help teams to detect issues earlier, reduce delays, and increase efficiency. For example, in dermatology trials where subject-reported outcomes are collected via electronic diaries, pre-emptive planning can mitigate the risks of missing data due to device failure or non-compliance. Teams are then able to implement contingency plans, protecting data integrity and enhancing the overall study experience for participants.
Well-documented estimand definitions also support readiness for regulatory inspections. When intercurrent event handling is clearly specified in the protocol, and further defined in the statistical analysis plan, it reduces the likelihood of queries from reviewers and provides a transparent audit trail that reinforces data integrity. When intercurrent events are well understood by all stakeholders, site teams are better equipped to identify and escalate issues, while central monitoring can identify patterns in real time.
Applications in early-phase development
ICH E9(R1) is formally enforced by regulatory authorities for Phase 2b and 3 studies, but the benefits of estimand thinking are becoming increasingly clear earlier in development. For instance, Phase 2a studies can benefit greatly from well-defined estimands since they inform registrational trial design or appear in regulatory briefing documents. These trials often test critical assumptions around dose response, endpoint variability and population selection, all of which require clarity for downstream interpretation.
Looking further ahead, there is growing interest in how estimands might add value in Phase 1 studies. These trials typically focus on safety and pharmacokinetics, with small cohorts and descriptive methods. While estimands are rarely applied in this context today, the underlying principles around the identification of intercurrent events that affect safety outcomes could help to improve the consistency and reliability of early-phase data by better controlling the possible confounding effect of intercurrent events on the preliminary safety profile of compounds. As trial designs become more integrated and adaptive, the earlier adoption of estimands may not only improve operational planning, but also help to ensure a smoother transition into later development stages.
Clarity drives confidence
The estimand framework provides a robust structure for aligning trial design, as well as conducting and analyzing clinical trials. Used well, it increases the reliability of results by bringing greater clarity, focus, and consistency to every stage of development. By prioritizing simplicity, encouraging cross-functional collaboration, and supporting education, the clinical research community can ensure estimands remain a practical, impactful tool.
Whether in early-phase exploration or late-stage confirmation, estimand planning should be viewed not as a compliance requirement, but as a mindset that supports better science, smarter operations, and stronger evidence for every development decision. This not only improves scientific rigor, but accelerates the delivery of safe, effective therapies to the patients who need them most.
