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Clinical Trials01:16

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Defining Clinical Trial Estimands: A Practical Guide for Study Teams with Examples Based on a Psychiatric Disorder.

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Therapeutic Innovation & Regulatory Science
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

Implementing the estimand framework requires interdisciplinary collaboration. This paper details a process for defining estimands in clinical trials, using major depressive disorder examples to guide stakeholders.

Keywords:
DepressionEstimatorICH E9(R1)Intercurrent eventsMajor depressive disorderMissing dataStakeholderTreatment effect

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Area of Science:

  • Clinical Trials Methodology
  • Pharmaceutical Research
  • Regulatory Science

Background:

  • The ICH E9(R1) Addendum on Estimands and Sensitivity Analysis was released in 2019.
  • Widespread implementation and stakeholder engagement are still developing.
  • Case studies with clinical and regulatory feedback are needed.

Purpose of the Study:

  • To describe an interdisciplinary process for implementing the estimand framework.
  • To illustrate this process with hypothetical clinical trial examples for major depressive disorder.
  • To address challenges in defining and reporting estimands.

Main Methods:

  • Devised an interdisciplinary process by the Estimands and Missing Data Working Group.
  • Used a template for estimand examples, covering stakeholder identification, decision-making, and key questions.
  • Incorporated diverse endpoints (continuous, binary, time-to-event) and strategies for handling intercurrent events.

Main Results:

  • Presented detailed examples of estimand specifications for hypothetical trials.
  • Included trial design, implementation elements, and estimator specifications.
  • Demonstrated the application of the estimand framework across various scenarios.

Conclusions:

  • Emphasized the necessity of multi-disciplinary collaboration for effective ICH E9(R1) framework implementation.
  • Highlighted the practical application of the estimand framework in clinical trial design.
  • Provided a structured approach for defining and reporting estimands in complex trials.