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Baylis describes herself as agnostic about heritable gene editing (otherwise known as germline gene editing) in the technology's present state, after providing a scientifically balanced and complete discussion of the ethics of the technology itself. Although proponents of heritable genome editing counter that it involves gene editing, not people editing, this is a specious argument: the genes are being edited in (future) people, not fruitflies. For example, she accurately distinguishes heritable genome editing, involving germline cells such as eggs and sperm and carried forward through successive generations, from somatic genome editing, performed on non-germline cells in a single individual and not heritable. [Extracted from the article]

Chris Barker I Statistical Planning and Analysis Services, Inc. i References 1 Bourla, A. (2020), "An Open Letter from Pfizer Chairman and CEO Albert Bourla", Pfizer investor news Open letter. This book is essential for statisticians, clinicians, Pharma QA, clinical operations, data management, regulatory, senior management, CEO's and many others to understand chartering, and operation of the Data and Safety Monitoring Board (DSMB). Some publicly traded companies have a black-out period during the conduct of an interim where all employees are forbidden from trading stock and from sharing internal corporate news about DSMB activities while a DSMB review is underway or about to occur. [Extracted from the article]

Statistical methods for survival trial design - With applications to cancer clinical trials using R On the other hand, survival analysis textbooks typically do not cover much materials on sample size calculation, which leave graduate students who wish to learn how to design survival trials little choices. The book I Statistical Methods for Survival Trial Design i is an important and useful addition to the literature, which combines survival analysis theory and cancer clinical trials design and monitoring cohesively. [Extracted from the article]

Chapter 9 (Special Issues and Resolutions) provides very brief descriptions for a number of different topics (e.g., drop-the-loser designs based on efficacy and safety, a comparison of the Fisher's vs. The book has a number of detailed examples, and SAS and R code to implement some of the methods described in the text, but no exercises or datasets. While it was not meant to be used as a textbook, some of the chapters (such as Chapters 1-4) certainly deserve to be on the recommended readings list for a graduate level class on clinical trials. [Extracted from the article]