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While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework., (© 2023. The Author(s).)

Adaptive design (AD) clinical trials use accumulating subject data to modify the parameters of the design of an ongoing study, without compromising the validity and integrity of the study. The 2010 US Food and Drug Administration (FDA) Draft Guidance on Adaptive Design Clinical Trials described a subset of 7 primary design types as "less well-understood." FDA defined these designs as those with limited regulatory experience. To better understand the properties of these less well-understood ADs and to promote their use when applicable, the Best Practices Subteam for DIA's Adaptive Design Scientific Working Group conducted an extensive nonsystematic search and reviewed trials from multiple sponsors who had employed these designs. Here, we review 10 specific case studies for which less well-understood ADs were employed and share feedback about their challenges and successes, as well as details about the regulatory interactions from these trials. We learned that these designs and associated statistical methodologies can make difficult research situations more amenable for study and, therefore, are needed in our toolbox. While they can be used to study many diseases, they are particularly valuable for rare diseases, small populations, studies involving terminal illnesses, and vaccine trials, in which it is important to find efficient ways to bring effective treatments to market more rapidly. It is imperative, however, that these methodologies be utilized appropriately, which requires careful planning and precise operational execution.