{"title":"The Role of Statistical Thinking in Biopharmaceutical Research","authors":"F. Bretz, J. Greenhouse","doi":"10.1080/19466315.2023.2224259","DOIUrl":null,"url":null,"abstract":"Abstract The development of new drugs has evolved dramatically over the past decade. Advances in technology enable scientists to generate “big data” faster than ever before. The availability of complex, high-volume data in turn creates demand for innovative quantitative solutions and tools in a rapidly evolving landscape. As a result, the role of the statistical scientist in collaborative research has never been more important. Reflecting on these changes, Cox (2012) wrote, “…[A]lthough the tactics of statistical analysis have been utterly changed… the strategy of research design and analysis has been much less affected…” In this article, we argue that the practice of statistics is built on the foundation of good statistical thinking and consists of a complex combination of problem-solving skills, the essence of what Cox meant by the “strategy of research.” Although others have highlighted the role of statistical thinking in research design and analysis, in the age of data science, machine learning and artificial intelligence, it cannot be emphasized enough. We outline four general steps that contribute to good statistical thinking and illustrate them with five use cases (“vignettes”) as well as a detailed case study discussion from a maintenance therapy clinical trial for depression.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Biopharmaceutical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/19466315.2023.2224259","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract The development of new drugs has evolved dramatically over the past decade. Advances in technology enable scientists to generate “big data” faster than ever before. The availability of complex, high-volume data in turn creates demand for innovative quantitative solutions and tools in a rapidly evolving landscape. As a result, the role of the statistical scientist in collaborative research has never been more important. Reflecting on these changes, Cox (2012) wrote, “…[A]lthough the tactics of statistical analysis have been utterly changed… the strategy of research design and analysis has been much less affected…” In this article, we argue that the practice of statistics is built on the foundation of good statistical thinking and consists of a complex combination of problem-solving skills, the essence of what Cox meant by the “strategy of research.” Although others have highlighted the role of statistical thinking in research design and analysis, in the age of data science, machine learning and artificial intelligence, it cannot be emphasized enough. We outline four general steps that contribute to good statistical thinking and illustrate them with five use cases (“vignettes”) as well as a detailed case study discussion from a maintenance therapy clinical trial for depression.
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.
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