Regression Methods In Biostatistics

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Regression Methods in Biostatistics

Author : Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch
Publisher : Springer Science & Business Media
Page : 512 pages
File Size : 43,5 Mb
Release : 2012-03-06
Category : Medical
ISBN : 9781461413530

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Regression Methods in Biostatistics by Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch Pdf

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.

Regression Methods in Biostatistics

Author : Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 42,7 Mb
Release : 2006-03-30
Category : Mathematics
ISBN : 9780387272559

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Regression Methods in Biostatistics by Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch Pdf

Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.

Regression Methods in Biostatistics

Author : Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch
Publisher : Springer Science & Business Media
Page : 526 pages
File Size : 51,5 Mb
Release : 2011-09
Category : Mathematics
ISBN : 9781461413523

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Regression Methods in Biostatistics by Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch Pdf

This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.

Regression Methods in Biostatistics

Author : Eric Vittinghoff
Publisher : Unknown
Page : 15 pages
File Size : 53,9 Mb
Release : 2004
Category : Medicine research-Statistical methods
ISBN : OCLC:794604462

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Regression Methods in Biostatistics by Eric Vittinghoff Pdf

Bayesian and Frequentist Regression Methods

Author : Jon Wakefield
Publisher : Springer Science & Business Media
Page : 700 pages
File Size : 52,7 Mb
Release : 2013-01-04
Category : Mathematics
ISBN : 9781441909251

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Bayesian and Frequentist Regression Methods by Jon Wakefield Pdf

Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines.

Topics in Biostatistics

Author : Walter T. Ambrosius
Publisher : Springer Science & Business Media
Page : 530 pages
File Size : 40,9 Mb
Release : 2007-07-06
Category : Medical
ISBN : 9781588295316

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Topics in Biostatistics by Walter T. Ambrosius Pdf

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.

Biostatistics

Author : Ronald N. Forthofer,Eun Sul Lee
Publisher : Elsevier
Page : 567 pages
File Size : 53,5 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781483296746

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Biostatistics by Ronald N. Forthofer,Eun Sul Lee Pdf

The Biostatistics course is often found in the schools of public Health, medical schools, and, occasionally, in statistics and biology departments. The population of students in these courses is a diverse one, with varying preparedness. The book assumes the reader has at least two years of high school algebra, but no previous exposure to statistics is required. Written for individuals who might be fearful of mathematics, this book minimizes the technical difficulties and emphasizes the importance of statistics in scientific investigation. An understanding of underlying design and analysis is stressed. The limitations of the research, design and analytical techniques are discussed, allowing the reader to accurately interpret results. Real data, both processed and raw, are used extensively in examples and exercises. Statistical computing packages - MINITAB, SAS and Stata - are integrated. The use of the computer and software allows a sharper focus on the concepts, letting the computer do the necessary number-crunching. * Emphasizes underlying statistical concepts more than competing texts * Focuses on experimental design and analysis, at an elementary level * Includes an introduction to linear correlation and regression * Statistics are central: probability is downplayed * Presents life tables and survival analysis * Appendix with solutions to many exercises * Special instructor's manual with solution to all exercises

Biostatistical Methods

Author : John M. Lachin
Publisher : John Wiley & Sons
Page : 672 pages
File Size : 52,5 Mb
Release : 2014-08-22
Category : Mathematics
ISBN : 9781118625842

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Biostatistical Methods by John M. Lachin Pdf

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.

Biostatistics and Computer-based Analysis of Health Data using Stata

Author : Christophe Lalanne,Mounir Mesbah
Publisher : Elsevier
Page : 134 pages
File Size : 53,5 Mb
Release : 2016-09-06
Category : Computers
ISBN : 9780081010846

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Biostatistics and Computer-based Analysis of Health Data using Stata by Christophe Lalanne,Mounir Mesbah Pdf

This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research. The use of Stata for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epideomological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential Stata packages and commands. Provides detailed examples of the use of Stata for common biostatistical tasks in medical research Features a work program structured around the four previous chapters and a series of practical exercises with commented corrections Includes an appendix to help the reader familiarize themselves with additional packages and commands Focuses on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data

Handbook of Regression and Modeling

Author : Daryl S. Paulson
Publisher : CRC Press
Page : 520 pages
File Size : 44,6 Mb
Release : 2006-12-19
Category : Mathematics
ISBN : 9781420017380

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Handbook of Regression and Modeling by Daryl S. Paulson Pdf

Carefully designed for use by clinical and pharmaceutical researchers and scientists, Handbook of Regression Analysis and Modeling explores statistical methods that have been adapted into biological applications for the quickly evolving field of biostatistics. The author clearly delineates a six-step method for hypothesis testing using data that mimics real life. Relying heavily on computer software, he includes exploratory data analysis to evaluate the fit of the model to the actual data. The book presents a well-defined procedure for adding or subtracting independent variables to the model variable and covers how to apply statistical forecasting methods to the serially correlated data characteristically found in clinical and pharmaceutical settings. The stand alone chapters allow you to pick and choose which chapter to read first and home in on the information that fits your immediate needs. Each example is presented in computer software format. The author uses MINITAB in the book but supplies instructions for SAS and SPSSX, making the book easily adaptable to individual situations. Although written with the assumption that the reader has knowledge of basic and matrix algebra, the book supplies a short course on matrix algebra in the appendix for those who need it. Covering more than just statistical theory, the book provides advanced methods that you can put to immediate use.

Regression Methods in Biostatistics

Author : Springer
Publisher : Unknown
Page : 532 pages
File Size : 45,8 Mb
Release : 2012-03-07
Category : Electronic
ISBN : 1461413540

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Regression Methods in Biostatistics by Springer Pdf

Robust Methods in Biostatistics

Author : Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser
Publisher : John Wiley & Sons
Page : 292 pages
File Size : 51,7 Mb
Release : 2009-05-11
Category : Medical
ISBN : 047074054X

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Robust Methods in Biostatistics by Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser Pdf

Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Statistical Methods for Biostatistics and Related Fields

Author : Wolfgang Härdle,Yuichi Mori,Philippe Vieu
Publisher : Springer Science & Business Media
Page : 373 pages
File Size : 40,9 Mb
Release : 2006-11-24
Category : Medical
ISBN : 9783540326915

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Statistical Methods for Biostatistics and Related Fields by Wolfgang Härdle,Yuichi Mori,Philippe Vieu Pdf

This book covers a wide range of recent statistical methods that are of interest to scientists in biostatistics as well as in other related fields such as chemometrics, environmetrics and geophysics. The contributed papers, from internationally recognized researchers, present various statistical methodologies together with a selected scope of their main mathematical properties and their application in a real case study.

Regression Modeling Strategies

Author : Frank E. Harrell
Publisher : Springer Science & Business Media
Page : 583 pages
File Size : 43,6 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475734621

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Regression Modeling Strategies by Frank E. Harrell Pdf

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Nonparametric Regression Methods for Longitudinal Data Analysis

Author : Hulin Wu,Jin-Ting Zhang
Publisher : John Wiley & Sons
Page : 384 pages
File Size : 55,8 Mb
Release : 2006-05-12
Category : Mathematics
ISBN : 9780470009666

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Nonparametric Regression Methods for Longitudinal Data Analysis by Hulin Wu,Jin-Ting Zhang Pdf

Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.