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Author: John M. Lachin
Publisher: John Wiley & Sons
ISBN: 1118625846
Category : Mathematics
Languages : en
Pages : 672
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Book Description
Praise for the First Edition ". . . an excellent textbook . . . an indispensable referencefor biostatisticians and epidemiologists." —International Statistical Institute A new edition of the definitive guide to classical and modernmethods of biostatistics Biostatistics consists of various quantitative techniques thatare essential to the description and evaluation of relationshipsamong biologic and medical phenomena. Biostatistical Methods:The Assessment of Relative Risks, Second Edition develops basicconcepts and derives an expanded array of biostatistical methodsthrough the application of both classical statistical tools andmore modern likelihood-based theories. With its fluid and balancedpresentation, the book guides readers through the importantstatistical methods for the assessment of absolute and relativerisks in epidemiologic studies and clinical trials withcategorical, count, and event-time data. Presenting a broad scope of coverage and the latest research onthe topic, the author begins with categorical data analysis methodsfor cross-sectional, prospective, and retrospective studies ofbinary, polychotomous, and ordinal data. Subsequent chapterspresent modern model-based approaches that include unconditionaland conditional logistic regression; Poisson and negative binomialmodels for count data; and the analysis of event-time dataincluding the Cox proportional hazards model and itsgeneralizations. The book now includes an introduction to mixedmodels with fixed and random effects as well as expanded methodsfor evaluation of sample size and power. Additional new topicsfeatured in this Second Edition include: Establishing equivalence and non-inferiority Methods for the analysis of polychotomous and ordinal data,including matched data and the Kappa agreement index Multinomial logistic for polychotomous data and proportionalodds models for ordinal data Negative binomial models for count data as an alternative tothe Poisson model GEE models for the analysis of longitudinal repeated measuresand multivariate observations Throughout the book, SAS is utilized to illustrate applicationsto numerous real-world examples and case studies. A related websitefeatures all the data used in examples and problem sets along withthe author's SAS routines. Biostatistical Methods, Second Edition is an excellentbook for biostatistics courses at the graduate level. It is also aninvaluable reference for biostatisticians, applied statisticians,and epidemiologists.
Author: John M. Lachin
Publisher: John Wiley & Sons
ISBN: 1118625846
Category : Mathematics
Languages : en
Pages : 672
Get Book
Book Description
Praise for the First Edition ". . . an excellent textbook . . . an indispensable referencefor biostatisticians and epidemiologists." —International Statistical Institute A new edition of the definitive guide to classical and modernmethods of biostatistics Biostatistics consists of various quantitative techniques thatare essential to the description and evaluation of relationshipsamong biologic and medical phenomena. Biostatistical Methods:The Assessment of Relative Risks, Second Edition develops basicconcepts and derives an expanded array of biostatistical methodsthrough the application of both classical statistical tools andmore modern likelihood-based theories. With its fluid and balancedpresentation, the book guides readers through the importantstatistical methods for the assessment of absolute and relativerisks in epidemiologic studies and clinical trials withcategorical, count, and event-time data. Presenting a broad scope of coverage and the latest research onthe topic, the author begins with categorical data analysis methodsfor cross-sectional, prospective, and retrospective studies ofbinary, polychotomous, and ordinal data. Subsequent chapterspresent modern model-based approaches that include unconditionaland conditional logistic regression; Poisson and negative binomialmodels for count data; and the analysis of event-time dataincluding the Cox proportional hazards model and itsgeneralizations. The book now includes an introduction to mixedmodels with fixed and random effects as well as expanded methodsfor evaluation of sample size and power. Additional new topicsfeatured in this Second Edition include: Establishing equivalence and non-inferiority Methods for the analysis of polychotomous and ordinal data,including matched data and the Kappa agreement index Multinomial logistic for polychotomous data and proportionalodds models for ordinal data Negative binomial models for count data as an alternative tothe Poisson model GEE models for the analysis of longitudinal repeated measuresand multivariate observations Throughout the book, SAS is utilized to illustrate applicationsto numerous real-world examples and case studies. A related websitefeatures all the data used in examples and problem sets along withthe author's SAS routines. Biostatistical Methods, Second Edition is an excellentbook for biostatistics courses at the graduate level. It is also aninvaluable reference for biostatisticians, applied statisticians,and epidemiologists.
Author: S. Selvin
Publisher: Brooks/Cole
ISBN:
Category : Mathematics
Languages : en
Pages : 530
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Book Description
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.
Author: Mark Chang
Publisher: Springer Science & Business Media
ISBN: 144199842X
Category : Medical
Languages : en
Pages : 316
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Book Description
Classic biostatistics, a branch of statistical science, has as its main focus the applications of statistics in public health, the life sciences, and the pharmaceutical industry. Modern biostatistics, beyond just a simple application of statistics, is a confluence of statistics and knowledge of multiple intertwined fields. The application demands, the advancements in computer technology, and the rapid growth of life science data (e.g., genomics data) have promoted the formation of modern biostatistics. There are at least three characteristics of modern biostatistics: (1) in-depth engagement in the application fields that require penetration of knowledge across several fields, (2) high-level complexity of data because they are longitudinal, incomplete, or latent because they are heterogeneous due to a mixture of data or experiment types, because of high-dimensionality, which may make meaningful reduction impossible, or because of extremely small or large size; and (3) dynamics, the speed of development in methodology and analyses, has to match the fast growth of data with a constantly changing face. This book is written for researchers, biostatisticians/statisticians, and scientists who are interested in quantitative analyses. The goal is to introduce modern methods in biostatistics and help researchers and students quickly grasp key concepts and methods. Many methods can solve the same problem and many problems can be solved by the same method, which becomes apparent when those topics are discussed in this single volume.
Author: Dr Murray Logan
Publisher: John Wiley & Sons
ISBN: 144436247X
Category : Science
Languages : en
Pages : 578
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Book Description
R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphing exploratory data analysis and graphical summaries regression (linear, multi and non-linear) simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures) frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.
Author: Stephen W. Looney
Publisher: Humana
ISBN: 9781617372711
Category : Science
Languages : en
Pages : 0
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Book Description
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.
Author: Stephen W. Looney
Publisher: Springer Science & Business Media
ISBN: 1592592422
Category : Science
Languages : en
Pages : 221
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Book Description
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.
Author: John M. Lachin
Publisher: John Wiley & Sons
ISBN: 0470317892
Category : Mathematics
Languages : en
Pages : 568
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Book Description
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.
Author: Ralph B. D'Agostino
Publisher: John Wiley & Sons
ISBN: 047002366X
Category : Mathematics
Languages : en
Pages : 466
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Book Description
The Tutorials in Biostatistics have become a very popular feature of the prestigious Wiley journal, Statistics in Medicine (SIM). The introductory style and practical focus make them accessible to a wide audience including medical practitioners with limited statistical knowledge. This book represents the first of two volumes presenting the best tutorials published in SIM, focusing on statistical methods in clinical studies. Topics include the design and analysis of clinical trials, epidemiology, survival analysis, and data monitoring. Each tutorial is focused on a medical problem, has been fully peer-reviewed and edited, and is authored by leading researchers in biostatistics. Many articles include an appendix on the latest developments since publication in the journal and additional references. This will appeal to statisticians working in medical research, as well as statistically-minded clinicians, biologists, epidemiologists and geneticists. It will also appeal to graduate students of biostatistics.
Author: Walter T. Ambrosius
Publisher: Springer Science & Business Media
ISBN: 1588295311
Category : Medical
Languages : en
Pages : 530
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Book Description
This book presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. It introduces advanced methods in statistics, including how to choose and work with statistical packages. Specific topics of interest include microarray analysis, missing data techniques, power and sample size, statistical methods in genetics. The book is an essential resource for researchers at every level of their career.
Author: Xinguang Chen
Publisher: Springer Nature
ISBN: 3030352609
Category : Medical
Languages : en
Pages : 420
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Book Description
This book examines statistical methods and models used in the fields of global health and epidemiology. It includes methods such as innovative probability sampling, data harmonization and encryption, and advanced descriptive, analytical and monitory methods. Program codes using R are included as well as real data examples. Contemporary global health and epidemiology involves a myriad of medical and health challenges, including inequality of treatment, the HIV/AIDS epidemic and its subsequent control, the flu, cancer, tobacco control, drug use, and environmental pollution. In addition to its vast scales and telescopic perspective; addressing global health concerns often involves examining resource-limited populations with large geographic, socioeconomic diversities. Therefore, advancing global health requires new epidemiological design, new data, and new methods for sampling, data processing, and statistical analysis. This book provides global health researchers with methods that will enable access to and utilization of existing data. Featuring contributions from both epidemiological and biostatistical scholars, this book is a practical resource for researchers, practitioners, and students in solving global health problems in research, education, training, and consultation.
