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Regression Methods In Biostatistics

Author: Eric Vittinghoff
Publisher: Springer Science & Business Media
ISBN: 1461413532
Size: 50.12 MB
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This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided.

The Evaluation Of Surrogate Endpoints

Author: Geert Molenberghs
Publisher: Springer Science & Business Media
ISBN: 9780387202778
Size: 29.56 MB
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The identification and use of surrogate endpoints, i.e., measures that can replace or supplement other endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and scepticism. This book offers a balanced account on this controversial topic.

Intuitive Biostatistics

Author: Harvey Motulsky
Publisher: Oxford University Press, USA
ISBN: 0199946647
Size: 59.45 MB
Format: PDF, ePub, Docs
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Thoroughly revised and updated, the third edition of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking retains and refines the core perspectives of the previous editions: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. With its engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. It is especially useful for those students in health-science related fields who have no background in biostatistics. NEW TO THIS EDITION * A new chapter on the complexities of probability * A new chapter on meta-analysis * A completely rewritten chapter on statistical traps to avoid * More sections on common mistakes in data analysis * More Q&A sections * New topics and examples * New learning tools (each chapter ends with a summary and a list of statistical terms)

Statistical Modeling For Biomedical Researchers

Author: William D. Dupont
Publisher: Cambridge University Press
ISBN: 1139643819
Size: 33.66 MB
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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/.

Textbook Of Cancer Epidemiology

Author: Hans-Olov Adami
Publisher: Oxford University Press
ISBN: 0190676841
Size: 36.73 MB
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"Comprehensive and comprehensible, but also encouraging -- informed by the hope and belief that informed its creation." -Cancer Amid sweeping advances in the science and treatment of cancer, the TEXTBOOK OF CANCER EPIDEMIOLOGY offers students and professionals a definitive, systematic resource for understanding the factors affecting all types of human cancer. This fully updated new edition offers an overview of epidemiology's key concepts and methods as they relate to cancer (including the emerging potential of biomarkers) as well as site-specific chapters on individual cancers' natural history, pathology, descriptive epidemiology, and etiology. Taken together, these chapters forge connections between established science and the ongoing evolution of this dynamic field. Crisply and concisely written by an assembly of internationally recognized researchers, the TEXTBOOK OF CANCER EPIDEMIOLOGY offers a superlative introduction to the subject's consensuses and controversies for those embarking on their careers and a ready reference for seasoned professionals.

Generalized Linear Mixed Models

Author: Charles E. McCulloch
Publisher: IMS
ISBN: 9780940600546
Size: 45.61 MB
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Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models

Spatial Analysis In Epidemiology

Author: Dirk Pfeiffer
Publisher: Oxford University Press
ISBN: 019850988X
Size: 13.58 MB
Format: PDF
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Providing a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology, this book examines spatial analytical methods in conjunction with GIS and remotely sensed data to provide insights into the patterns and processes that underlie disease transmission.

Fundamentals Of Biostatistics

Author: Bernard Rosner
Publisher: Cengage Learning
ISBN: 1133008178
Size: 61.16 MB
Format: PDF, Kindle
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Bernard Rosner's FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Biostatistics A Computing Approach

Author: Stewart Anderson
Publisher: CRC Press
ISBN: 1584888342
Size: 54.46 MB
Format: PDF, ePub
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The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding. As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided. Teaching by example, this book emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work. Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.