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Biostatistical Methods

Author: John M. Lachin
Publisher: John Wiley & Sons
ISBN: 1118625846
Size: 24.27 MB
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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.

Biostatistical Methods

Author: John M. Lachin
Publisher: Wiley-Interscience
ISBN:
Size: 26.19 MB
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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.

Randomization In Clinical Trials

Author: William F. Rosenberger
Publisher: John Wiley & Sons
ISBN: 111874215X
Size: 47.97 MB
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Praise for the First Edition “All medical statisticians involved in clinical trials should read this book…” - Controlled Clinical Trials Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, Randomization in Clinical Trials: Theory and Practice, Second Edition is the go-to guide for biostatisticians and pharmaceutical industry statisticians. Randomization in Clinical Trials: Theory and Practice, Second Edition features: Discussions on current philosophies, controversies, and new developments in the increasingly important role of randomization techniques in clinical trials A new chapter on covariate-adaptive randomization, including minimization techniques and inference New developments in restricted randomization and an increased focus on computation of randomization tests as opposed to the asymptotic theory of randomization tests Plenty of problem sets, theoretical exercises, and short computer simulations using SAS® to facilitate classroom teaching, simplify the mathematics, and ease readers’ understanding Randomization in Clinical Trials: Theory and Practice, Second Edition is an excellent reference for researchers as well as applied statisticians and biostatisticians. The Second Edition is also an ideal textbook for upper-undergraduate and graduate-level courses in biostatistics and applied statistics. William F. Rosenberger, PhD, is University Professor and Chairman of the Department of Statistics at George Mason University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and author of over 80 refereed journal articles, as well as The Theory of Response-Adaptive Randomization in Clinical Trials, also published by Wiley. John M. Lachin, ScD, is Research Professor in the Department of Epidemiology and Biostatistics as well as in the Department of Statistics at The George Washington University. A Fellow of the American Statistical Association and the Society for Clinical Trials, Dr. Lachin is actively involved in coordinating center activities for clinical trials of diabetes. He is the author of Biostatistical Methods: The Assessment of Relative Risks, Second Edition, also published by Wiley.

Statistical Methods For Immunogenicity Assessment

Author: Harry Yang
Publisher: CRC Press
ISBN: 1498700357
Size: 57.58 MB
Format: PDF
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Develop Effective Immunogenicity Risk Mitigation Strategies Immunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation strategies. Statistical Methods for Immunogenicity Assessment provides a single source of information on statistical concepts, principles, methods, and strategies for detection, quantification, assessment, and control of immunogenicity. The book first gives an overview of the impact of immunogenicity on biopharmaceutical development, regulatory requirements, and statistical methods and strategies used for immunogenicity detection, quantification, and risk assessment and mitigation. It then covers anti-drug antibody (ADA) assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation. The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies. They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. A critical issue in the development of biologics, immunogenicity can cause early termination or limited use of the products if not managed well. This book shows how to use robust statistical methods for detecting, quantifying, assessing, and mitigating immunogenicity risk. It is an invaluable resource for anyone involved in immunogenicity risk assessment and control in both non-clinical and clinical biopharmaceutical development.

Tutorials In Biostatistics Statistical Methods In Clinical Studies

Author: Ralph B. D'Agostino
Publisher: John Wiley & Sons
ISBN: 047002366X
Size: 26.93 MB
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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.

Statistical Methods In Spatial Epidemiology

Author: Andrew B. Lawson
Publisher: John Wiley & Sons
ISBN: 1118723171
Size: 22.64 MB
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Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

Applied Categorical Data Analysis And Translational Research

Author: Chap T. Le
Publisher: John Wiley & Sons
ISBN: 0470371307
Size: 49.70 MB
Format: PDF, Mobi
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An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational research Thoroughly updated with the latest advances in the field, Applied Categorical Data Analysis and Translational Research, Second Edition maintains the accessible style of its predecessor while also exploring the importance of translational research as it relates to basic scientific findings within clinical practice. With its easy–to–follow style, updated coverage of major methodologies, and broadened scope of coverage, this new edition provides an accessible guide to statistical methods involving categorical data and the steps to their application in problem solving in the biomedical sciences. Delving even further into the applied direction, this update offers many real–world examples from biomedicine, epidemiology, and public health along with detailed case studies taken straight from modern research in these fields. Additional features of the Second Edition include: A new chapter on the relationship between translational research and categorical data, focusing on design study, bioassay, and Phase I and Phase II clinical trials A new chapter on categorical data and diagnostic medicine, with coverage of the diagnostic process, prevalence surveys, the ROC function and ROC curve, and important statistical considerations A revised chapter on logistic regression models featuring an updated treatment of simple and multiple regression analysis An added section on quantal bioassays Each chapter features updated and new exercise sets along with numerous graphs that demonstrate the highly visual nature of the topic. A related Web site features the book′s examples as well as additional data sets that can be worked with using SAS® software. The only book of its kind to provide balanced coverage of methods for both categorical data and translational research, Applied Categorical Data Analysis and Translational Research, Second Edition is an excellent book for courses on applied statistics and biostatistics at the upper–undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the biomedical and public health fields.

Statistical Rules Of Thumb

Author: Gerald van Belle
Publisher: John Wiley & Sons
ISBN: 1118210360
Size: 79.23 MB
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Praise for the First Edition: "For a beginner [this book] is a treasure trove; for anexperienced person it can provide new ideas on how better to pursuethe subject of applied statistics." —Journal of Quality Technology Sensibly organized for quick reference, Statistical Rules ofThumb, Second Edition compiles simple rules that arewidely applicable, robust, and elegant, and each captures keystatistical concepts. This unique guide to the use of statisticsfor designing, conducting, and analyzing research studiesillustrates real-world statistical applications through examplesfrom fields such as public health and environmental studies. Alongwith an insightful discussion of the reasoning behind everytechnique, this easy-to-use handbook also conveys the variouspossibilities statisticians must think of when designing andconducting a study or analyzing its data. Each chapter presents clearly defined rules related toinference, covariation, experimental design, consultation, and datarepresentation, and each rule is organized and discussed under fivesuccinct headings: introduction; statement and illustration of therule; the derivation of the rule; a concluding discussion; andexploration of the concept's extensions. The author also introducesnew rules of thumb for topics such as sample size for ratioanalysis, absolute and relative risk, ANCOVA cautions, anddichotomization of continuous variables. Additional features of theSecond Edition include: Additional rules on Bayesian topics New chapters on observational studies and Evidence-BasedMedicine (EBM) Additional emphasis on variation and causation Updated material with new references, examples, and sources A related Web site provides a rich learning environment andcontains additional rules, presentations by the author, and amessage board where readers can share their own strategies anddiscoveries. Statistical Rules of Thumb, SecondEdition is an ideal supplementary book for courses inexperimental design and survey research methods at theupper-undergraduate and graduate levels. It also serves as anindispensable reference for statisticians, researchers,consultants, and scientists who would like to develop anunderstanding of the statistical foundations of their researchefforts. A related website www.vanbelle.org provides additionalrules, author presentations and more.

Statistical Analysis Of Designed Experiments

Author: Ajit C. Tamhane
Publisher: John Wiley & Sons
ISBN: 1118491432
Size: 37.48 MB
Format: PDF, Kindle
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A indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests. Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets. With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.

Introductory Biostatistics

Author: Chap T. Le
Publisher: John Wiley & Sons
ISBN: 0471458562
Size: 18.38 MB
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Provides many real-data sets in various fields in the form of examples at at the end of all twelve chapters in the form of exercises. Covers all of the nuts and bolts of biostatistics in a user-friendly style that motivates readers. Contains notes on computations at the end of most chapters, covering the use of Excel, SAS, and others.