Biostatistical Methods
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Biostatistical Methods
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Author : John M. Lachin
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-25
Biostatistical Methods written by John M. Lachin 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 2009-09-25 with Mathematics categories.
Comprehensive coverage of classical and modern methods of biostatistics Biostatistical Methods focuses on the assessment of risks and relative risks on the basis of clinical investigations. It develops basic concepts and derives biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories. The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models. The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; and the analysis of event time data, including the proportional hazards and multiplicative intensity models. The book contains a technical appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods. Biostatistical Methods: The Assessment of Relative Risks: * Presents modern biostatistical methods that are generalizations of the classical methods discussed * Emphasizes derivations, not just cookbook methods * Provides copious reference citations for further reading * Includes extensive problem sets * Employs case studies to illustrate application of methods * Illustrates all methods using the Statistical Analysis System(r) (SAS) Supplemented with numerous graphs, charts, and tables as well as a Web site for larger data sets and exercises, Biostatistical Methods: The Assessment of Relative Risks is an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.
Biostatistical Methods
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Author : Stephen W. Looney
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-03
Biostatistical Methods written by Stephen W. Looney 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 2008-02-03 with Science categories.
Leading biostatisticians and biomedical researchers describe many of the key techniques used to solve commonly occurring data analytic problems in molecular biology, and demonstrate how these methods can be used in the development of new markers for exposure to a risk factor or for disease outcomes. Major areas of application include microarray analysis, proteomic studies, image quantitation, genetic susceptibility and association, evaluation of new biomarkers, and power analysis and sample size.
Biostatistical Methods In Epidemiology
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Author : Stephen C. Newman
language : en
Publisher: John Wiley & Sons
Release Date : 2003-04-11
Biostatistical Methods In Epidemiology written by Stephen C. Newman 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 2003-04-11 with Medical categories.
An introduction to classical biostatistical methods in epidemiology Biostatistical Methods in Epidemiology provides an introduction to a wide range of methods used to analyze epidemiologic data, with a focus on nonregression techniques. The text includes an extensive discussion of measurement issues in epidemiology, especially confounding. Maximum likelihood, Mantel-Haenszel, and weighted least squares methods are presented for the analysis of closed cohort and case-control data. Kaplan-Meier and Poisson methods are described for the analysis of censored survival data. A justification for using odds ratio methods in case-control studies is provided. Standardization of rates is discussed and the construction of ordinary, multiple decrement and cause-deleted life tables is outlined. Sample size formulas are given for a range of epidemiologic study designs. The text ends with a brief overview of logistic and Cox regression. Other highlights include: Many worked examples based on actual data Discussion of exact methods Recommendations for preferred methods Extensive appendices and references Biostatistical Methods in Epidemiology provides an excellent introduction to the subject for students, while also serving as a comprehensive reference for epidemiologists and other health professionals. For more information, visit www.wiley.com/mathematics
Practical Biostatistical Methods
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Author : S. Selvin
language : en
Publisher: Brooks/Cole
Release Date : 1995
Practical Biostatistical Methods written by S. Selvin and has been published by Brooks/Cole this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Mathematics categories.
This text covers intermediate statistical methods in a practical and mathematically intuitive (no calculus) mode. It focuses on the following analysis methods useful to the researcher: linear regression, discriminant analysis, contingency tables, survival analysis, covariance, principal components, logistic regression and Poisson regression. Nonparametric methods are incorporated as needed.
Biostatistical Methods In Genetics And Genetic Epidemiology An Introduction To Genetic Epidemiology Biostatistical Genetics
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Author : Lynn Decollblanc
language : en
Publisher: Lynn Decollblanc
Release Date : 2023-06-19
Biostatistical Methods In Genetics And Genetic Epidemiology An Introduction To Genetic Epidemiology Biostatistical Genetics written by Lynn Decollblanc and has been published by Lynn Decollblanc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-19 with Medical categories.
Biostatistical Methods in Genetics and Genetic Epidemiology offers a comprehensive exploration of the powerful intersection between biostatistics, genetics, and epidemiology. This authoritative book delves into the fundamental principles and advanced methodologies that drive genetic research and enable a deeper understanding of the complex relationship between genes, diseases, and population health. With a focus on practical applications and real-world examples, this book equips readers with the necessary tools to navigate the vast landscape of genomic analysis. From unraveling genetic variation to investigating disease risk factors and exploring the role of environmental factors, this book provides a solid foundation for researchers, practitioners, and students in the fields of biostatistics, genetics, epidemiology, and related disciplines. Key Features: Comprehensive Coverage: Explore the essential concepts, techniques, and study designs used in genetic research, including case-control studies, linkage analysis, genome-wide association studies, and more. Statistical Methods: Gain a deep understanding of the statistical principles and methodologies employed in genetic analysis, including statistical genetics, genetic risk prediction, and analysis of complex traits. Genetic Epidemiology: Examine the integration of genetics and epidemiology, uncovering how genetic factors interact with environmental and lifestyle factors to influence disease outcomes and population health. Practical Examples: Benefit from real-world examples and case studies that demonstrate the application of biostatistical methods in unraveling the genetic underpinnings of diseases, identifying genetic risk factors, and informing public health interventions. Ethical and Legal Considerations: Explore the ethical implications and challenges surrounding genetic research, genetic counseling, privacy, and data sharing. Emerging Trends: Stay up-to-date with the latest advancements in the field, including precision medicine, pharmacogenomics, epigenetics, and the integration of multi-omics data. Biostatistical Methods in Genetics and Genetic Epidemiology is an invaluable resource for researchers, practitioners, and students seeking to unravel the complexities of genetics and its impact on human health. Whether you are a biostatistician, geneticist, epidemiologist, or medical professional, this book will empower you to navigate the rapidly evolving field of genomics and make meaningful contributions to precision medicine, disease prevention, and public health initiatives.
Modern Biostatistical Methods For Evidence Based Global Health Research
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Author : Ding-Geng (Din) Chen
language : en
Publisher: Springer Nature
Release Date : 2022-11-25
Modern Biostatistical Methods For Evidence Based Global Health Research written by Ding-Geng (Din) Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Medical categories.
This book provides an overview of the emerging topics in biostatistical theories and methods through their applications to evidence-based global health research and decision-making. It brings together some of the top scholars engaged in biostatistical method development on global health to highlight and describe recent advances in evidence-based global health applications. The volume is composed of five main parts: data harmonization and analysis; systematic review and statistical meta-analysis; spatial-temporal modeling and disease mapping; Bayesian statistical modeling; and statistical methods for longitudinal data or survival data. It is designed to be illuminating and valuable to both expert biostatisticians and to health researchers engaged in methodological applications in evidence-based global health research. It is particularly relevant to countries where global health research is being rigorously conducted.
Frontiers Of Biostatistical Methods And Applications In Clinical Oncology
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Author : Shigeyuki Matsui
language : en
Publisher: Springer
Release Date : 2017-10-03
Frontiers Of Biostatistical Methods And Applications In Clinical Oncology written by Shigeyuki Matsui and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-03 with Medical categories.
This book presents the state of the art of biostatistical methods and their applications in clinical oncology. Many methodologies established today in biostatistics have been brought about through its applications to the design and analysis of oncology clinical studies. This field of oncology, now in the midst of evolution owing to rapid advances in biotechnologies and cancer genomics, is becoming one of the most promising disease fields in the shift toward personalized medicine. Modern developments of diagnosis and therapeutics of cancer have also been continuously fueled by recent progress in establishing the infrastructure for conducting more complex, large-scale clinical trials and observational studies. The field of cancer clinical studies therefore will continue to provide many new statistical challenges that warrant further progress in the methodology and practice of biostatistics. This book provides a systematic coverage of various stages of cancer clinical studies. Topics from modern cancer clinical trials include phase I clinical trials for combination therapies, exploratory phase II trials with multiple endpoints/treatments, and confirmative biomarker-based phase III trials with interim monitoring and adaptation. It also covers important areas of cancer screening, prognostic analysis, and the analysis of large-scale molecular data in the era of big data.
Modern Applied Biostatistical Methods
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Author : Steve Selvin
language : en
Publisher: Oxford University Press
Release Date : 1998-02-19
Modern Applied Biostatistical Methods written by Steve Selvin and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-02-19 with Medical categories.
Statistical analysis typically involves applying theoretically generated techniques to the description and interpretation of collected data. In this text, theory, application and interpretation are combined to present the entire biostatistical process for a series of elementary and intermediate analytic methods. The theoretical basis for each method is discussed with a minimum of mathematics and is applied to a research data example using a computer system called S-PLUS. This system produces concrete numerical results and increases one's understanding of the fundamental concepts and methodology of statistical analysis. Combining statistical logic, data and computer tools, the author explores such topics as random number generation, general linear models, estimation, analysis of tabular data, analysis of variance and survival analysis. The end result is a clear and complete explanation of the way statistical methods can help one gain an understanding of collected data. Modern Applied Biostatistical Methods is unlike other statistical texts, which usually deal either with theory or with applications. It integrates the two elements into a single presentation of theoretical background, data, interpretation, graphics, and implementation. This all-around approach will be particularly helpful to students in various biostatistics and advanced epidemiology courses, and will interest all researchers involved in biomedical data analysis. This text is not a computer manual, even though it makes extensive use of computer language to describe and illustrate applied statistical techniques. This makes the details of the statistical process readily accessible, providing insight into how and why a statistical method identifies the properties of sampled data. The first chapter gives a simple overview of the S-PLUS language. The subsequent chapters use this valuable statistical tool to present a variety of analytic approaches.
Statistical Research Methods
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Author : Roy Sabo
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-10-22
Statistical Research Methods written by Roy Sabo 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 2013-10-22 with Medical categories.
This textbook will help graduate students in non-statistics disciplines, advanced undergraduate researchers, and research faculty in the health sciences to learn, use and communicate results from many commonly used statistical methods. The material covered, and the manner in which it is presented, describe the entire data analysis process from hypothesis generation to writing the results in a manuscript. Chapters cover, among other topics: one and two-sample proportions, multi-category data, one and two-sample means, analysis of variance, and regression. Throughout the text, the authors explain statistical procedures and concepts using a non-statistical language. This accessible approach is complete with real-world examples and sample write-ups for the Methods and Results sections of scholarly papers. The text also allows for the concurrent use of the programming language R, which is an open-source program created, maintained and updated by the statistical community. R is freely available and easy to download.
Understanding Advanced Statistical Methods
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Author : Peter Westfall
language : en
Publisher: CRC Press
Release Date : 2013-04-09
Understanding Advanced Statistical Methods written by Peter Westfall and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-09 with Mathematics categories.
Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book’s website.