The need for effective clinical trial dialogue is imperative to ensure clarity, consistency, and alignment with stakeholders. A comprehensive framework for effective clinical trial not only facilitates this but also helps in enhancing trial outcomes and decision-making. An estimand may be used when conducting health-related studies or interventions to help clarify how to interpret treatment effects. It can also be useful in clarifying what the research question is with respect to the study in order to avoid misinterpretation while also ensuring that study methods are in line with the study objectives. In order to structure and harmonize the way in which clinical trial-related dialogue is shared between sponsors and regulators, the International Council for Harmonization (ICH) developed the E9(R1) Estimand Framework.
The Implementation of the E9(R1) Estimand Framework is a major step forward in the planning, analysis, and interpretation of clinical trials. It was developed by the ICH, and it seeks to handle complications that arise due to intercurrent events (those events that occur post-treatment initiation and influence how the clinical outcomes might be interpreted or measured).
An estimand is a precise description of the treatment effect that accounts for intercurrent events. It consists of four attributes:
- Population: The group of patients targeted by the trial.
- Variable: The outcome measure of interest.
- Intercurrent Events: Events that may alter the interpretation or occurrence of the outcome.
- Summary Measure: This method is used to summarize the treatment effect.
Clinical Outcomes and Intercurrent Events in Phase 3 Randomized Controlled Trial Data
Randomized controlled trials are the base for regulatory approval of drugs, as well as evidence-based medicine and policy. In the process of developing new drugs, regulatory trials help determine the impact of an intervention on a given standard of care. It is anticipated that every participant will complete the trial in accordance with the protocol and provide all necessary data, as these studies aim to create practically balanced treatment groups.
The goal is to show significant clinical benefits while managing various complexities, including intercurrent events. These events, such as treatment discontinuation, initiation of rescue medication, or patient non-compliance, can complicate the interpretation of treatment effects.
For instance, where there is a trial to test the efficacy of new cancer therapy, an estimand could describe how many patients stopped treatment early because of adverse effects of new cancer therapy on overall survival.
Modeling the Efficacy Outcomes and Intercurrent Events
The E9(R1) Estimand Framework must account for efficacy outcomes and intercurrent events. Statistical approaches like joint modeling or time-to-event analysis are available to examine the effect of intervening events on clinical outcomes. For example, through joint modeling, longitudinal outcome data can be simultaneously analyzed together with time-to-intercurrent events to produce a holistic understanding of treatment effect.
Such models can be used to differentiate between the types of intercurrent events and their impacts. For example, a dropout due to adverse events might represent a different type of risk profile compared to a dropout occurring due to the lack of efficacy of the intervention. Modeling of these will offer a better insight into the true effect of intervention, making the results more informative and accurate.
Enabling Analytical and Simulation Studies
Analytical and simulation studies evaluate a range of estimands and strategies using data sets generated from well-specified data-generating models. Analytical studies help explore the theoretical properties and convergent ways of differentiating the statistical methods in a controlled environment. On the other hand, simulation studies can evaluate the behavior of estimands in more realistic and complex settings with varying populations of patients and treatments.
These investigations are important in determining the most appropriate way to handle intercurrent events, and thus ensure the estimand chosen is the most applicable to the clinical question of interest. They also help characterize the stability and sensitivity of the estimands to the assumptions and the data. This characterization is critical for both regulatory submissions and clinical decisions.
Implementing the E9(R1) Estimand Framework
Implementing the E9(R1) Estimand Framework follows a systematic approach with major stakeholders:
Clear Objectives and Estimands– Describe the primary clinical question of interest and corresponding estimands clearly while ensuring alignment with stakeholders.
Intercurrent Events- List all potential events that impact trial outcomes like treatment changes and specify how each will be managed.
Training and Education- Train personnel on the E9(R1) framework by covering new statistical methods and tools for effective implementation.
Statistical Analysis Plans- Create detailed plans (SAPs) that integrate chosen estimands by drafting methods for handling events and defining primary and secondary analyses.
Analytical and Simulation Studies- Test estimands and methods through studies to refine plans and address potential issues.
Engaging with Regulatory Authorities- Collaborate early with regulators to align estimands with their conjecture, using pre-submission meetings for feedback.
Reporting and Documentation- Document estimand definitions, analysis plans, and outcomes precisely, disclosing event management and impacts on results transparently.
Conclusion
The implementation of E9(R1) Estimand Framework provides the pharmaceutical industry and regulatory agencies clarity in assessing trial results by using guidance to address intercurrent events. This is a way forward to increase the reliability of results from clinical trials for better therapeutic decisions and ultimately better patient outcomes.
Application of the Estimand Framework in clinical practice will help moving closer to achieve the full potential of precision medicine.
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References and Further Reading
- Brennan C Kahan, Joanna Hindley, Mark Edwards, Suzie Cro, Tim P Morris, The estimands framework: a primer on the ICH E9(R1) addendum, BMJ 2024.
- Alexei C. Ionan, Miya Paterniti, Devan V Mehrotra, John Scott, Clinical and Statistical Perspectives on the ICH E9(R1) Estimand Framework Implementation, 2022.
- ICH E9: Statistical Principles for Clinical Trials
- ICH E9 (R1): Addendum: Statistical Principles for Clinical Trials