Mathematical and Statistical Skills in the Biopharmaceutical Industry最新文献

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Data Management 数据管理
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-4
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
Background and Motivation 背景与动机
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-1
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
Introduction to Chapters 2, 3, and 4 第二、三、四章导言
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-2
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
Development of New Validated Scoring Systems 开发新的有效的评分系统
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-7
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
Statistical Programming 统计编程
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-3
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 1
Can One Predict Unpredictable? 人能预测不可预测吗?
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-9
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
Resurrecting a Failed Clinical Program 复活一个失败的临床项目
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-8
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
Biostatistics 生物统计学
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-5
A. Pitman, O. Sverdlov, L. Pearce
{"title":"Biostatistics","authors":"A. Pitman, O. Sverdlov, L. Pearce","doi":"10.1201/9780429155352-5","DOIUrl":"https://doi.org/10.1201/9780429155352-5","url":null,"abstract":"This course addresses issues related to the design, conduct, analysis, and interpretation of clinical trials. Topics include protocol development, examination and selection of appropriate experimental design, methods of randomization, sample size determination, appropriate methods of data analysis including time-to-event (possibly censored) data, non-inferiority studies, and interim monitoring and ethical issues. Prerequisites: EPH 505 or equivalent, and second-year status. in translational Open Online estimation and monitoring; data management and quality assurance procedures; cost-effectiveness and quality of life; and case studies of vaccine trials, factorial trials, primary and secondary prevention trials, large simple trials, strategy trials, and cost-effectiveness. The case studies include many of the classical and landmark clinical trials, such as the polio vaccine field trial, Physicians Health Study, and the trials of coronary artery surgery. Prerequisite: EPH 505. 1 Course cr BIS 562b, Clinical Decision Support Edward Melnick and Mona Sharifi Building on BIS 560/CB&B 740, this course provides the purpose, scope, and history of decision support systems within health care. Using a weekly hands-on application of knowledge acquired in the lecture portion of the course, students identify a clinical need and prototype their own clinical decision support solution. Solutions are then presented in a “shark tank” format to iteratively refine them to yield a final product that is capable of real-world implementation. Prerequisite: BIS 560 or CB&B 740. statistical material computational theoretic soware used The course presents methods for the design and analysis of studies arising in the implementation and prevention science space. These studies tend to implement a range of cluster-randomized designs, quasi-experimental designs, and observational designs. This course consists of two parts. The first provides an exposition of the theory and analytic techniques used in the design and analysis of experimental studies arising in implementation and prevention science. The second covers the design and analysis of quasi-experimental and observational studies. SAS/R is used for problem sets. Prerequisites: S&DS 541 and S&DS 542 or equivalents, or permission of the instructor. designs where the treatment is assigned based on a cut-off rule on test scores or poverty indexes and hence is affected by the lack of overlap. The second part of the course focuses on ways to go beyond the treatment effect and investigate all the mechanisms that come into play when the intervention is implemented: causal pathways, spillover effects, and heterogeneity. A better understanding of these mechanisms can help us improve the design of our intervention. We first learn statistical methods to disentangle causal pathways through which the intervention has an effect. We then relax the common assumption of independence between units and allow the treatment of one unit to affect the outcome of","PeriodicalId":386154,"journal":{"name":"Mathematical and Statistical Skills in the Biopharmaceutical Industry","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124202568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Introduction to Chapters 5, 6, and 7 第5、6、7章导论
Mathematical and Statistical Skills in the Biopharmaceutical Industry Pub Date : 2019-07-15 DOI: 10.1201/9780429155352-6
A. Pitman, O. Sverdlov, L. Pearce
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引用次数: 0
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