Statistical Modeling In Biomedical Research
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Statistical Modeling In Biomedical Research
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Author : Yichuan Zhao
language : en
Publisher: Springer Nature
Release Date : 2020-03-19
Statistical Modeling In Biomedical Research written by Yichuan Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-19 with Medical categories.
This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its applications.
Statistical Modeling In Biomedical Research
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Author :
language : en
Publisher:
Release Date : 2020
Statistical Modeling In Biomedical Research written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Biometry categories.
This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its applications.
Statistical Modeling For Biomedical Researchers
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Author : William D. Dupont
language : en
Publisher: Cambridge University Press
Release Date : 2009-02-12
Statistical Modeling For Biomedical Researchers written by William D. Dupont and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-12 with Medical categories.
A second edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.
Statistical Modeling For Biomedical Researchers
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Author : William Dudley Dupont
language : en
Publisher:
Release Date : 2014-05-14
Statistical Modeling For Biomedical Researchers written by William Dudley Dupont and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-14 with Medical categories.
New edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.
Statistical Modeling For Biomedical Researchers
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Author : William D. Dupont
language : en
Publisher: Cambridge University Press
Release Date : 2009-02-12
Statistical Modeling For Biomedical Researchers written by William D. Dupont and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-12 with Medical categories.
The second edition of this standard text guides biomedical researchers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is again used to perform the analyses, this time employing the much improved version 10 with its intuitive point and click as well as character-based commands. Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available at http://biostat.mc.vanderbilt.edu/dupontwd/wddtext/.
Applied Biostatistical Principles And Concepts
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Author : Laurens Holmes, Jr.
language : en
Publisher: Routledge
Release Date : 2017-11-22
Applied Biostatistical Principles And Concepts written by Laurens Holmes, Jr. and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-22 with Medical categories.
The past three decades have witnessed modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery Applied Biostatistical Principles and Concepts provides practical knowledge using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations. This book is suitable for both clinicians and health or biological sciences students. It presents the reality in statistical modelling of health research data in a concise manner that will address the issue of "big data" type I error tolerance and probability value, effect size and confidence interval for precision, effect measure modification and interaction as well as confounders, thus allowing for more valid inferences and yielding results that are more reliable, valid and accurate.
Handbook Of Statistical Bioinformatics
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Author : Henry Horng-Shing Lu
language : en
Publisher: Springer Nature
Release Date : 2022-12-08
Handbook Of Statistical Bioinformatics written by Henry Horng-Shing Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Science categories.
Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.
Statistical Models And Methods For Biomedical And Technical Systems
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Author : Filia Vonta
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-05
Statistical Models And Methods For Biomedical And Technical Systems written by Filia Vonta 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 2008-03-05 with Medical categories.
This book deals with the mathematical aspects of survival analysis and reliability as well as other topics, reflecting recent developments in the following areas: applications in epidemiology; probabilistic and statistical models and methods in reliability; models and methods in survival analysis, longevity, aging, and degradation; accelerated life models; quality of life; new statistical challenges in genomics. The work will be useful to a broad interdisciplinary readership of researchers and practitioners in applied probability and statistics, industrial statistics, biomedicine, biostatistics, and engineering.
Statistical Learning For Biomedical Data
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Author : James D. Malley
language : en
Publisher: Cambridge University Press
Release Date : 2011-02-24
Statistical Learning For Biomedical Data written by James D. Malley and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-24 with Medical categories.
This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random ForestsTM, neural nets, support vector machines, nearest neighbors and boosting.
Regression Methods For Medical Research
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Author : Bee Choo Tai
language : en
Publisher: John Wiley & Sons
Release Date : 2013-10-09
Regression Methods For Medical Research written by Bee Choo Tai and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-09 with Medical categories.
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures. The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the key design questions posed and in so doing take due account of any effects of potentially influencing co-variables. It begins with a revision of basic statistical concepts, followed by a gentle introduction to the principles of statistical modelling. The various methods of modelling are covered in a non-technical manner so that the principles can be more easily applied in everyday practice. A chapter contrasting regression modelling with a regression tree approach is included. The emphasis is on the understanding and the application of concepts and methods. Data drawn from published studies are used to exemplify statistical concepts throughout. Regression Methods for Medical Research is especially designed for clinicians, public health and environmental health professionals, para-medical research professionals, scientists, laboratory-based researchers and students.