Estimand框架可以实现什么？

IF 1.5 4区医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Statistics in Biopharmaceutical Research Pub Date : 2023-01-30 DOI:10.1080/19466315.2023.2173645

Susan Mayo, Yongman Kim

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引用次数: 1

摘要

ICH E9(R1)评估指南是行业和监管统计人员创建和审查方案设计和分析计划的关键工具。该框架被描述为有助于改进研究设计、并发事件处理、数据收集、分析和解释，以使评估与主要临床问题保持一致，从而增加清晰度和准确性，以支持监管决策。在本文中，我们描述了我们作为监管统计学家在审查新药研究方案和统计分析计划方面的经验，重点是用于支持新药申请和生物许可证申请中有效性的实质性证据的试验。我们的目的是描述我们使用这个强大而有效的框架工具的经验，使临床试验的主要目标与其分析结果和解释保持一致。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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What Can Be Achieved with the Estimand Framework?

Abstract The ICH E9(R1) guidance on estimands is a key tool for the creation and review of protocol design and analysis planning, for both industry and regulatory statisticians. The framework has been described as useful for improving study design, intercurrent event handling, data collection, analysis, and interpretation to align the estimand with the primary clinical question to add clarity and precision to support regulatory decision-making. In this article, we describe our experience as regulatory statisticians in review of Investigational New Drug protocols and statistical analysis plans, with an emphasis on trials used to support substantial evidence of effectiveness in New Drug Applications and Biologic License Applications. Our intent is to describe our experience with this powerful and effective framework tool, to align the clinical trial’s primary objective with its analysis outcomes and interpretation.

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来源期刊

Statistics in Biopharmaceutical Research MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

3.90

自引率

16.70%

发文量

期刊介绍： Statistics in Biopharmaceutical Research ( SBR), publishes articles that focus on the needs of researchers and applied statisticians in biopharmaceutical industries; academic biostatisticians from schools of medicine, veterinary medicine, public health, and pharmacy; statisticians and quantitative analysts working in regulatory agencies (e.g., U.S. Food and Drug Administration and its counterpart in other countries); statisticians with an interest in adopting methodology presented in this journal to their own fields; and nonstatisticians with an interest in applying statistical methods to biopharmaceutical problems. Statistics in Biopharmaceutical Research accepts papers that discuss appropriate statistical methodology and information regarding the use of statistics in all phases of research, development, and practice in the pharmaceutical, biopharmaceutical, device, and diagnostics industries. Articles should focus on the development of novel statistical methods, novel applications of current methods, or the innovative application of statistical principles that can be used by statistical practitioners in these disciplines. Areas of application may include statistical methods for drug discovery, including papers that address issues of multiplicity, sequential trials, adaptive designs, etc.; preclinical and clinical studies; genomics and proteomics; bioassay; biomarkers and surrogate markers; models and analyses of drug history, including pharmacoeconomics, product life cycle, detection of adverse events in clinical studies, and postmarketing risk assessment; regulatory guidelines, including issues of standardization of terminology (e.g., CDISC), tolerance and specification limits related to pharmaceutical practice, and novel methods of drug approval; and detection of adverse events in clinical and toxicological studies. Tutorial articles also are welcome. Articles should include demonstrable evidence of the usefulness of this methodology (presumably by means of an application). The Editorial Board of SBR intends to ensure that the journal continually provides important, useful, and timely information. To accomplish this, the board strives to attract outstanding articles by seeing that each submission receives a careful, thorough, and prompt review. Authors can choose to publish gold open access in this journal.