Advanced Statistics In Regulatory Critical Clinical Initiatives
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| Author | : Wei Zhang |
| Publisher | : CRC Press |
| Total Pages | : 318 |
| Release | : 2022-05-25 |
| Genre | : Mathematics |
| ISBN | : 1000567990 |
Advanced Statistics in Regulatory Critical Clinical Initiatives is focused on the critical clinical initiatives introduced by the 21st Century Cure Act passed by the United States Congress in December 2016. The book covers everything from the outline of the initiatives to analysis on the effect on biopharmaceutical research and development. Advanced Statistics in Regulatory Critical Clinical Initiatives provides innovative ways to resolve common challenges in statistical research of rare diseases such small sample sizes and provides guidance for combined use of data. With analysis from regulatory and scientific perspectives this book is an ideal companion for researchers in biostatistics, pharmaceutical development, and policy makers in related fields. Key Features: Provides better understanding of innovative design and analysis of each critical clinical initiatives which may be used in regulatory review/approval of drug development. Makes recommendations to evaluate submissions accurately and reliably. Proposes innovative study designs and statistical methods for oncology and/or rare disease drug development. Provides insight regarding current regulatory guidance on drug development such as gene therapy and rare diseases.
| Author | : Trevor F. Cox |
| Publisher | : CRC Press |
| Total Pages | : 334 |
| Release | : 2022-06-23 |
| Genre | : Mathematics |
| ISBN | : 1000601102 |
Cancer is a dreaded disease. One in two people will be diagnosed with cancer within their lifetime. Medical Statistics for Cancer Studies shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It gives some background in cancer biology and genetics, followed by detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics. It includes lots of examples using real data from the author’s many years of experience working in a cancer clinical trials unit. Features: A broad and accessible overview of statistical methods in cancer research Necessary background in cancer biology and genetics Details of statistical methodology with minimal algebra Many examples using real data from cancer clinical trials Appendix giving statistics revision.
| Author | : Oleksandr Sverdlov |
| Publisher | : CRC Press |
| Total Pages | : 462 |
| Release | : 2022-12-06 |
| Genre | : Mathematics |
| ISBN | : 1000799239 |
One of the hallmarks of the 21st century medicine is the emergence of digital therapeutics (DTx)—evidence-based, clinically validated digital technologies to prevent, diagnose, treat, and manage various diseases and medical conditions. DTx solutions have been gaining interest from patients, investors, healthcare providers, health authorities, and other stakeholders because of the potential of DTx to deliver equitable, massively scalable, personalized and transformative treatments for different unmet medical needs. Digital Therapeutics: Scientific, Statistical, Clinical, and Regulatory Aspects is an unparalleled summary of the current scientific, statistical, developmental, and regulatory aspects of DTx which is poised to become the fastest growing area of the biopharmaceutical and digital medicine product development. This edited volume intends to provide a systematic exposition to digital therapeutics through 19 peer-reviewed chapters written by subject matter experts in this emerging field. This edited volume is an invaluable resource for business leaders and researchers working in public health, healthcare, digital health, information technology, and biopharmaceutical industries. It will be also useful for regulatory scientists involved in the review of DTx products, and for faculty and students involved in an interdisciplinary research on digital health and digital medicine. Key Features: Provides the taxonomy of the concepts and a navigation tool for the field of DTx. Covers important strategic aspects of the DTx industry, thereby helping investors, developers, and regulators gain a better appreciation of the potential value of DTx. Expounds on many existing and emerging state-of-the art scientific and technological tools, as well as data privacy, ethical and regulatory considerations for DTx product development. Presents several case studies of successful development of some of the most remarkable DTx products. Provides some perspectives and forward-looking statements on the future of digital medicine.
| Author | : Jeffrey Wilson |
| Publisher | : CRC Press |
| Total Pages | : 280 |
| Release | : 2023-03-27 |
| Genre | : Business & Economics |
| ISBN | : 1000848825 |
This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data, the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research.
| Author | : Paul Faya |
| Publisher | : CRC Press |
| Total Pages | : 423 |
| Release | : 2022-12-15 |
| Genre | : Mathematics |
| ISBN | : 1000824861 |
The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter). The collection of case studies covers a broad array of CMC topics, including stability analysis, analytical method development, specification setting, process development and optimization, process control, experimental design, dissolution testing, and comparability studies. The analysis of each case study includes a presentation of code and reproducible output. This book is written with an academic level aimed at practicing nonclinical biostatisticians, most of whom have graduate degrees in statistics. • First book of its kind focusing strictly on CMC Bayesian case studies • Case studies with code and output • Representation from several companies across the industry as well as academia • Authors are leading and well-known Bayesian statisticians in the CMC field • Accompanying website with code for reproducibility • Reflective of real-life industry applications/problems
| Author | : Harry Yang |
| Publisher | : CRC Press |
| Total Pages | : 335 |
| Release | : 2022-10-04 |
| Genre | : Business & Economics |
| ISBN | : 100065267X |
The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise
| Author | : Song Zhang |
| Publisher | : CRC Press |
| Total Pages | : 215 |
| Release | : 2023-05-16 |
| Genre | : Medical |
| ISBN | : 1000873552 |
This book begins with an introduction of pragmatic cluster randomized trials (PCTs) and reviews various pragmatic issues that need to be addressed by statisticians at the design stage. It discusses the advantages and disadvantages of each type of PCT, and provides sample size formulas, sensitivity analyses, and examples for sample size calculation. The generalized estimating equation (GEE) method will be employed to derive sample size formulas for various types of outcomes from the exponential family, including continuous, binary, and count variables. Experimental designs that have been frequently employed in PCTs will be discussed, including cluster randomized designs, matched-pair cluster randomized design, stratified cluster randomized design, stepped-wedge cluster randomized design, longitudinal cluster randomized design, and crossover cluster randomized design. It demonstrates that the GEE approach is flexible to accommodate pragmatic issues such as hierarchical correlation structures, different missing data patterns, randomly varying cluster sizes, etc. It has been reported that the GEE approach leads to under-estimated variance with limited numbers of clusters. The remedy for this limitation is investigated for the design of PCTs. This book can assist practitioners in the design of PCTs by providing a description of the advantages and disadvantages of various PCTs and sample size formulas that address various pragmatic issues, facilitating the proper implementation of PCTs to improve health care. It can also serve as a textbook for biostatistics students at the graduate level to enhance their knowledge or skill in clinical trial design. Key Features: Discuss the advantages and disadvantages of each type of PCTs, and provide sample size formulas, sensitivity analyses, and examples. Address an unmet need for guidance books on sample size calculations for PCTs; A wide variety of experimental designs adopted by PCTs are covered; The sample size solutions can be readily implemented due to the accommodation of common pragmatic issues encountered in real-world practice; Useful to both academic and industrial biostatisticians involved in clinical trial design; Can be used as a textbook for graduate students majoring in statistics and biostatistics.
| Author | : Christos Nakas |
| Publisher | : CRC Press |
| Total Pages | : 234 |
| Release | : 2023-05-15 |
| Genre | : Mathematics |
| ISBN | : 1482233711 |
This book presents a unified and up-to-date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The emphasis is on the conceptual underpinning of ROC analysis and the practical implementation in diverse scientific fields. A plethora of examples accompany the methodologic discussion using standard statistical software such as R and STATA. The book arrives after two decades of intensive growth in both the methods and the applications of ROC analysis and presents a new synthesis. The authors provide a contemporary, integrated exposition of ROC methodology for both classification and prediction and include material on multiple-class ROC. This book avoids lengthy technical exposition and provides code and datasets in each chapter. Receiver Operating Characteristic Analysis for Classification and Prediction is intended for researchers and graduate students, but will also be useful for those that use ROC analysis in diverse disciplines such as diagnostic medicine, bioinformatics, medical physics, and perception psychology.
| Author | : Ying Yuan |
| Publisher | : CRC Press |
| Total Pages | : 234 |
| Release | : 2022-11-11 |
| Genre | : Medical |
| ISBN | : 0429628471 |
Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol—making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges, issues, and solutions arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustrations of using software to design trials Develops a companion website (www.trialdesign.org) to provide freely available, easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the design, conduct, and optimization of early-phase dose-finding trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!
| Author | : Marie Reilly |
| Publisher | : CRC Press |
| Total Pages | : 436 |
| Release | : 2023-05-26 |
| Genre | : Mathematics |
| ISBN | : 0429588801 |
This book covers classic epidemiological designs that use a reference/control group, including case-control, case-cohort, nested case-control and variations of these designs, such as stratified and two-stage designs. It presents a unified view of these sampling designs as representations of an underlying cohort or target population of interest. This enables various extended designs to be introduced and analysed with a similar approach: extreme sampling on the outcome (extreme case-control design) or on the exposure (exposure-enriched, exposure-density, countermatched), designs that re-use prior controls and augmentation sampling designs. Further extensions exploit aggregate data for efficient cluster sampling, accommodate time-varying exposures and combine matched and unmatched controls. Self-controlled designs, including case-crossover, self-controlled case series and exposure-crossover, are also presented. The test-negative design for vaccine studies and the use of negative controls for bias assessment are introduced and discussed. This book is intended for graduate students in biostatistics, epidemiology and related disciplines, or for health researchers and data analysts interested in extending their knowledge of study design and data analysis skills. This book Bridges the gap between epidemiology and the more mathematically oriented biostatistics books. Assembles the wealth of epidemiological knowledge about observational study designs that is scattered over several decades of scientific publications. Illustrates the performance of methods in real research applications. Provides guidelines for implementation in standard software packages (Stata, R). Includes numerous exercises, covering simple mathematical proofs, consideration of proposed or published designs, and practical data analysis.
