Mixture Modelling For Medical And Health Sciences
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Mixture Modelling For Medical And Health Sciences
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Author : Shu-Kay Ng
language : en
Publisher: CRC Press
Release Date : 2019-05-03
Mixture Modelling For Medical And Health Sciences written by Shu-Kay Ng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-03 with Mathematics categories.
Mixture Modelling for Medical and Health Sciences provides a direct connection between theoretical developments in mixture modelling and their applications in real world problems. The book describes the development of the most important concepts through comprehensive analyses of real and practical examples taken from real-life research problems in
Medical Applications Of Finite Mixture Models
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Author : Peter Schlattmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-03-02
Medical Applications Of Finite Mixture Models written by Peter Schlattmann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-02 with Medical categories.
Patients are not alike! This simple truth is often ignored in the analysis of me- cal data, since most of the time results are presented for the “average” patient. As a result, potential variability between patients is ignored when presenting, e.g., the results of a multiple linear regression model. In medicine there are more and more attempts to individualize therapy; thus, from the author’s point of view biostatis- cians should support these efforts. Therefore, one of the tasks of the statistician is to identify heterogeneity of patients and, if possible, to explain part of it with known explanatory covariates. Finite mixture models may be used to aid this purpose. This book tries to show that there are a large range of applications. They include the analysis of gene - pression data, pharmacokinetics, toxicology, and the determinants of beta-carotene plasma levels. Other examples include disease clustering, data from psychophysi- ogy, and meta-analysis of published studies. The book is intended as a resource for those interested in applying these methods.
Mixture Models And Applications
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Author : Nizar Bouguila
language : en
Publisher: Springer
Release Date : 2019-08-13
Mixture Models And Applications written by Nizar Bouguila and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-13 with Technology & Engineering categories.
This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
Bayesian Analysis With R For Drug Development
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Author : Harry Yang
language : en
Publisher: CRC Press
Release Date : 2019-06-26
Bayesian Analysis With R For Drug Development written by Harry Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-26 with Mathematics categories.
Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development. Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical, clinical, and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.
Innovative Statistics In Regulatory Science
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Author : Shein-Chung Chow
language : en
Publisher: CRC Press
Release Date : 2019-11-18
Innovative Statistics In Regulatory Science written by Shein-Chung Chow and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-18 with Mathematics categories.
Statistical methods that are commonly used in the review and approval process of regulatory submissions are usually referred to as statistics in regulatory science or regulatory statistics. In a broader sense, statistics in regulatory science can be defined as valid statistics that are employed in the review and approval process of regulatory submissions of pharmaceutical products. In addition, statistics in regulatory science are involved with the development of regulatory policy, guidance, and regulatory critical clinical initiatives related research. This book is devoted to the discussion of statistics in regulatory science for pharmaceutical development. It covers practical issues that are commonly encountered in regulatory science of pharmaceutical research and development including topics related to research activities, review of regulatory submissions, recent critical clinical initiatives, and policy/guidance development in regulatory science. Devoted entirely to discussing statistics in regulatory science for pharmaceutical development. Reviews critical issues (e.g., endpoint/margin selection and complex innovative design such as adaptive trial design) in the pharmaceutical development and regulatory approval process. Clarifies controversial statistical issues (e.g., hypothesis testing versus confidence interval approach, missing data/estimands, multiplicity, and Bayesian design and approach) in review/approval of regulatory submissions. Proposes innovative thinking regarding study designs and statistical methods (e.g., n-of-1 trial design, adaptive trial design, and probability monitoring procedure for sample size) for rare disease drug development. Provides insight regarding current regulatory clinical initiatives (e.g., precision/personalized medicine, biomarker-driven target clinical trials, model informed drug development, big data analytics, and real world data/evidence). This book provides key statistical concepts, innovative designs, and analysis methods that are useful in regulatory science. Also included are some practical, challenging, and controversial issues that are commonly seen in the review and approval process of regulatory submissions. About the author Shein-Chung Chow, Ph.D. is currently a Professor at Duke University School of Medicine, Durham, NC. He was previously the Associate Director at the Office of Biostatistics, Center for Drug Evaluation and Research, United States Food and Drug Administration (FDA). Dr. Chow has also held various positions in the pharmaceutical industry such as Vice President at Millennium, Cambridge, MA, Executive Director at Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers Squibb, Plainsboro, NJ. He was elected Fellow of the American Statistical Association and an elected member of the ISI (International Statistical Institute). Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Biostatistics Book Series, Chapman and Hall/CRC Press, Taylor & Francis, New York. Dr. Chow is the author or co-author of over 300 methodology papers and 30 books.
Economic Evaluation Of Cancer Drugs
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Author : Iftekhar Khan
language : en
Publisher: CRC Press
Release Date : 2019-06-14
Economic Evaluation Of Cancer Drugs written by Iftekhar Khan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-14 with Mathematics categories.
Cancer is a major healthcare burden across the world and impacts not only the people diagnosed with various cancers but also their families, carers, and healthcare systems. With advances in the diagnosis and treatment, more people are diagnosed early and receive treatments for a disease where few treatments options were previously available. As a result, the survival of patients with cancer has steadily improved and, in most cases, patients who are not cured may receive multiple lines of treatment, often with financial consequences for the patients, insurers and healthcare systems. Although many books exist that address economic evaluation, Economic Evaluation of Cancer Drugs using Clinical Trial and Real World Data is the first unified text that specifically addresses the economic evaluation of cancer drugs. The authors discuss how to perform cost-effectiveness analyses while emphasising the strategic importance of designing cost-effectiveness into cancer trials and building robust economic evaluation models that have a higher chance of reimbursement if truly cost-effective. They cover the use of real-world data using cancer registries and discuss how such data can support or complement clinical trials with limited follow up. Lessons learned from failed reimbursement attempts, factors predictive of successful reimbursement and the different payer requirements across major countries including US, Australia, Canada, UK, Germany, France and Italy are also discussed. The book includes many detailed practical examples, case studies and thought-provoking exercises for use in classroom and seminar discussions. Iftekhar Khan is a medical statistician and health economist and a lead statistician at Oxford Unviersity’s Center for Statistics in Medicine. Professor Khan is also a Senior Research Fellow in Health Economics at University of Warwick and is a Senior Statistical Assessor within the Licensing Division of the UK Medicine and Health Regulation Agency. Ralph Crott is a former professor in Pharmacoeconomics at the University of Montreal in Quebec, Canada and former head of the EORTC Health Economics Unit and former senior health economist at the Belgian HTA organization. Zahid Bashir has over twelve years experience working in the pharmaceutical industry in medical affairs and oncology drug development where he is involved in the design and execution of oncology clinical trials and development of reimbursement dossiers for HTA submission.
Mathematical And Statistical Skills In The Biopharmaceutical Industry
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Author : Arkadiy Pitman
language : en
Publisher: CRC Press
Release Date : 2019-07-15
Mathematical And Statistical Skills In The Biopharmaceutical Industry written by Arkadiy Pitman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-15 with Business & Economics categories.
Mathematical and Statistical Skills in the Biopharmaceutical Industry: A Pragmatic Approach describes a philosophy of efficient problem solving showcased using examples pertinent to the biostatistics function in clinical drug development. It was written to share a quintessence of the authors’ experiences acquired during many years of relevant work in the biopharmaceutical industry. The book will be useful will be useful for biopharmaceutical industry statisticians at different seniority levels and for graduate students who consider a biostatistics-related career in this industry. Features: Describes a system of principles for pragmatic problem solving in clinical drug development. Discusses differences in the work of a biostatistician in small pharma and big pharma. Explains the importance/relevance of statistical programming and data management for biostatistics and necessity for integration on various levels. Describes some useful statistical background that can be capitalized upon in the drug development enterprise. Explains some hot topics and current trends in biostatistics in simple, non-technical terms. Discusses incompleteness of any system of standard operating procedures, rules and regulations. Provides a classification of scoring systems and proposes a novel approach for evaluation of the safety outcome for a completed randomized clinical trial. Presents applications of the problem solving philosophy in a highly problematic transfusion field where many investigational compounds have failed. Discusses realistic planning of open-ended projects.
Measuring Income Equity In The Demand For Healthcare With Finite Mixture Models
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Author : Галина Бесстремянная
language : en
Publisher: Litres
Release Date : 2022-01-29
Measuring Income Equity In The Demand For Healthcare With Finite Mixture Models written by Галина Бесстремянная and has been published by Litres this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-29 with Medical categories.
The paper exploits panel data finite mixture (latent class) models to measure consumer equity in healthcare access and utilization. The finite mixture approach accounts for unobservable consumer heterogeneity, while generalized linear models address a retransformation problem of logged dependent variable. Using the data of the Japan Household Panel Survey (2009–2014), we discover that consumers separate into latent classes in the binary choice models for healthcare use and generalized linear models for outpatient/inpatient healthcare expenditure. The results reveal that healthcare access in Japan is pro-poor for the most sick consumers, while utilization of outpatient care is equitable with respect to disposable income.
Disease Modelling And Public Health Part A
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Author :
language : en
Publisher: Elsevier
Release Date : 2017-10-13
Disease Modelling And Public Health Part A written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-13 with Mathematics categories.
Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy. - Presents a comprehensive, two-part volume written by leading subject experts - Provides a unique breadth and depth of content coverage - Addresses the most cutting-edge developments in the field - Includes chapters on Ebola and the Zika virus; topics which have grown in prominence and scholarly output
Advances In Statistical Methods For The Health Sciences
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Author : Jean-Louis Auget
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-08
Advances In Statistical Methods For The Health Sciences written by Jean-Louis Auget and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-08 with Mathematics categories.
Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained comprehensive volume covers a wide range of topics pertaining to new statistical methods in the health sciences, including epidemiology, pharmacovigilance, quality of life, survival analysis, and genomics. The book will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.