良好数据科学实践：迈向药物开发实践规范（复辩状）

IF 1.5 4区医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Statistics in Biopharmaceutical Research Pub Date : 2023-01-02 DOI:10.1080/19466315.2022.2128402

Mark Baillie, Conor Moloney, Carsten Philipp Mueller, Jonas Dorn, J. Branson, D. Ohlssen

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引用次数: 0

摘要

方和何问，为什么我们把重点放在探索性活动上（引用“[…]26次探索性[…]只有3次验证性[/.]”），而不是验证性活动，因此，我们的数据科学定义范围是否有限。他们还询问，数据科学的定义是否应该更具体，重点关注治疗效果：“探索性活动不足以确定治疗效果的存在性和估计治疗效果的大小，这在本质上是证实性的。”

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Good Data Science Practice: Moving toward a Code of Practice for Drug Development (Rejoinder)

Fang and He ask why we focus on exploratory (cite “[...] 26 times exploratory [...] only 3 times confirmatory [/.]”) over confirmatory activities and if as a consequence our data science definition is limited in scope. They also ask if the definition of data science should be more specific, with a focus on treatment effectiveness: “exploratory activities are insufficient for the purpose of establishing the existence and estimating the magnitude of treatment effects, which is confirmatory in nature.”

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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.