The regulatory implementation of the Estimand Framework in the U.S. context has evolved over time, addressing issues related to missing data in clinical trials. Starting from the pre-history of missing data recognition to the development of the ICH E9(R1) framework, the focus has been on aligning pl

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The concept of estimands in oncology clinical trials plays a crucial role in defining treatment effects and analyzing overall survival outcomes. This framework aims to enhance transparency, align trial objectives, and strengthen interdisciplinary dialogues. Introduced by the ICH E9(R1) guideline in

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Simulation studies are often used to evaluate statistical methods and study power, but they can sometimes produce misleading results. This work discusses strategies to assess and improve the quality of simulation studies, drawing on experiences and considerations outlined in relevant literature. A s

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Lack of clarity in trial objectives can lead to confusion among developers, decision-makers, patients, and prescribers. Estimands at ICH, presented by Rob Hemmings, address the need to focus on WHAT to estimate rather than just HOW. Patients differ in response to treatment, and multiple treatment ef

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Simulation studies in Stata involve using (pseudo) random numbers to generate data from a distribution for studying statistical methods. This process helps to evaluate different scenarios and understand the properties of statistical techniques. Key components like ADEMP (Aims, Data-generating mechan

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The field of oncology presents unique opportunities for advancing drug development through novel therapeutic modalities like immunotherapies, cell and gene therapies, and radioligand therapies. However, regulators, sponsors, and payers face criticism, often due to non-significant results in overall

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