About 8000 clinical trials are undertaken annually in all areas of medicine, from the treatment of acne to the prevention of cancer. Correct interpretation of the data from such trials depends largely on adequate design and on performing the appropriate statistical analyses. In this book, the statistical aspects of both the design and analysis of trials are described, with particular emphasis on recently developed methods of analysis.

Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter. After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis. Features: Presents an accessible introduction to multivariate analysis for behavioral scientists Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage Includes nearly 100 exercises for course use or self-study Supplemented by a GitHub repository with all datasets and R code for the examples and exercises Theoretical details are separated from the main body of the text Suitable for anyone working in the behavioral sciences with a basic grasp of statistics

Much of the data collected in medicine and the social sciences is categorical, for example, sex, marital status, blood group, whether a smoker or not and so on, rather than interval-scaled. Frequently the researcher collecting such data is interested in the relationships or associations between pairs, or between a set of such categorical variables; often the data is displayed in the form of a contingency table for example, smoker versus non-smoker against death from lung cancer or death from some other cause. This text gives a comprehensive account of the analysis of such tables, written at a level suitable for the applied researcher. The first edition of "The Analysis of Contingency Tables" arose from Professor A.E. Maxwell's earlier text, "Analysing Qualitative Data". In this new edition, more material is included that those methods which have developed over the last decade or so, for example, logistic regression models for tables with ordered categories and for response variables with more than two categories. A brief account is given of the increasingly important technique, correspondence analysis. The methods of analysis described in this book should be relevant to research workers and graduate students dealing with data from surveys, particularly in the area of psychiatry, social sciences and psychology.

Built around a problem solving theme, this book extends the intermediate and advanced student's expertise to more challenging situations that involve applying statistical methods to real-world problems. Data relevant to these problems are collected and analyzed to provide useful answers. Building on its central problem-solving theme, a large number of data sets arising from real problems are contained in the text and in the exercises provided at the end of each chapter. Answers, or hints to providing answers, are provided in an appendix. Concentrating largely on the established SPSS and the newer S-Plus statistical packages, the author provides a short, end-of-chapter section entitled Computer Hints that helps the student undertake the analyses reported in the chapter using these statistical packages.

Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudinal and survival data analysis, missing data, generalized additive models (GAMs), and Bayesian methods. The book focuses on performing these analyses using SAS, the software package of choice for those analysing medical data.

Features

• Covers the planning stage of medical studies in detail; several chapters contain details of sample size estimation
• Illustrates methods of randomisation that might be employed for clinical trials
• Covers topics that have become of great importance in the 21st century, including Bayesian methods and multiple imputation

Its breadth and depth, coupled with the inclusion of all the SAS code, make this book ideal for practitioners as well as for a graduate class in biostatistics or public health.

Complete data sets, all the SAS code, and complete outputs can be found on an associated website: http: //support.sas.com/amsus

This primer has been designed as a self-instructional text which serves to introduce the reader to both the principles of statistical modelling of covariance structures and to the use of the EQS software package. It is divided into three parts - the first covering the basic ideas and language of covariance structure modelling together with an introduction to the EQS package. The second section covers a wide variety of models suitable for cross-sectional and longitudinal data and the final section discusses a wide variety of practical problems. This book should be of interest to researchers in psychology, sociology and medicine who use the EQS software; applied and consultant statisticians.

Since the first edition of this book was published, S-PLUS has evolved markedly with new methods of analysis, new graphical procedures, and a convenient graphical user interface (GUI). Today, S-PLUS is the statistical software of choice for many applied researchers in disciplines ranging from finance to medicine. Combining the command line language and GUI of S-PLUS now makes this book even more suitable for inexperienced users, students, and anyone without the time, patience, or background needed to wade through the many more advanced manuals and texts on the market. The second edition of A Handbook of Statistical Analyses Using S-Plus has been completely revised to provide an outstanding introduction to the latest version of this powerful software system. Each chapter focuses on a particular statistical technique, applies it to one or more data sets, and shows how to generate the proposed analyses and graphics using S-PLUS. The author explains S-PLUS functions from both the Windows and command-line perspectives and clearly demonstrates how to switch between the two. This handbook provides the perfect vehicle for introducing the exciting possibilities S-PLUS, S-PLUS 2000, and S-PLUS 6 hold for data analysis. All of the data sets used in the text, along with script files giving the command language used in each chapter, are available for download from the Internet at http://www.iop.kcl.ac.uk/iop/Departments/BioComp/splus.shtml

Built around a problem solving theme, this book extends the intermediate and advanced student's expertise to more challenging situations that involve applying statistical methods to real-world problems. Data relevant to these problems are collected and analyzed to provide useful answers.
Building on its central problem-solving theme, a large number of data sets arising from real problems are contained in the text and in the exercises provided at the end of each chapter. Answers, or hints to providing answers, are provided in an appendix.
Concentrating largely on the established SPSS and the newer S-Plus statistical packages, the author provides a short, end-of-chapter section entitled "Computer Hints" that helps the student undertake the analyses reported in the chapter using these statistical packages.

A Handbook of Statistical Analyses Using SPSS clearly describes how to conduct a range of univariate and multivariate statistical analyses using the latest version of the Statistical Package for the Social Sciences, SPSS 11. Each chapter addresses a different type of analytical procedure applied to one or more data sets, primarily from the social and behavioral sciences areas. Each chapter also contains exercises relating to the data sets introduced, providing readers with a means to develop both their SPSS and statistical skills. Model answers to the exercises are also provided. Readers can download all of the data sets from a companion Web site furnished by the authors.

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis. New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses Whether you're a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.

Updated to reflect SAS 9.2, A Handbook of Statistical Analyses using SAS, Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS. Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis. They demonstrate the analyses through real-world examples, including methadone maintenance treatment, the relation of cirrhosis deaths to alcohol consumption, a sociological study of children, heart transplant treatment, and crime rate determinants. With the data sets and SAS code available online, this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.

Much of the data collected in medicine and the social sciences is categorical, for example, sex, marital status, blood group, whether a smoker or not and so on, rather than interval-scaled. Frequently the researcher collecting such data is interested in the relationships or associations between pairs, or between a set of such categorical variables; often the data is displayed in the form of a contingency table for example, smoker versus non-smoker against death from lung cancer or death from some other cause. This text gives a comprehensive account of the analysis of such tables, written at a level suitable for the applied researcher. The first edition of "The Analysis of Contingency Tables" arose from Professor A.E. Maxwell's earlier text, "Analysing Qualitative Data". In this new edition, more material is included that those methods which have developed over the last decade or so, for example, logistic regression models for tables with ordered categories and for response variables with more than two categories. A brief account is given of the increasingly important technique, correspondence analysis. The methods of analysis described in this book should be relevant to research workers and graduate students dealing with data from surveys, particularly in the area of psychiatry, social sciences and psychology.

The main message of this book is that people should be on their guard against both scare stories about risks to health, and claims for miracle cures of medical conditions. In the 21st century hardly a day passes without another article appearing in the media about a new treatment for a particular disease, new ways of improving our health by changing our lifestyle or new foodstuffs that claim to increase (or decrease) the risk of heart disease, cancer and the like. But how should the general public react to such claims, given that some of the journalists writing them focus on the sensational rather than the mundane and often have no qualms about sacrificing accuracy and honesty for the sake of a good story? Perhaps the wisest initial response is one of healthy scepticism, followed by an attempt to discover more about the details of the studies behind the reports. But most people are not, and have little desire to become experts in health research. By reading this book, however, these non-experts can, with minimal effort, learn enough about the scientific method to differentiate between those health claims, warnings and lifestyle recommendations that have some merit and those that are unproven or simply dishonest. So if you want to know if ginseng can really help with your erectile dysfunction, if breast cancer screening is all that politicians claim it to be, if ECT for depression is really a horror treatment and should be banned, if using a mobile phone can lead to brain tumours and how to properly evaluate the evidence from health and lifestyle related studies, then this is the book for you.

Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure. This intermediate-level textbook introduces the reader to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its 2nd edition, 'Applied Multivariate Data Analysis' has been fully expanded and updated, including major chapter revisions as well as new sections on neural networks and random effects models for longitudinal data. Maintaining the easy-going style of the first edition, the authors provide clear explanations of each technique, as well as supporting figures and examples, and minimal technical jargon. With extensive exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied statistics courses and applied researchers in many disciplines.

Applied statisticians often need to perform analyses of multivariate data; for these they will typically use one of the statistical software packages, S-Plus or R. This book sets out how to use these packages for these analyses in a concise and easy-to-use way, and will save users having to buy two books for the job. The author is well-known for this kind of book, and so buyers will trust that he 's got it right.

Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results. This requires the use of one or other of the many methods of multivariate analysis, and the use of a suitable software package such as S-PLUS or R.

In this book the core multivariate methodology is covered along with some basic theory for each method described. The necessary R and S-PLUS code is given for each analysis in the book, with any differences between the two highlighted. A website with all the datasets and code used in the book can be found at http: //biostatistics.iop.kcl.ac.uk/publications/everitt/.

Graduate students, and advanced undergraduates on applied statistics courses, especially those in the social sciences, will find this book invaluable in their work, and it will also be useful to researchers outside of statistics who need to deal with the complexities of multivariate data in their work.

Brian Everitt is Emeritus Professor of Statistics, Kinga (TM)s College, London.

With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, A Handbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language.

Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The core of the book centers on how to use Stata to perform analyses and how to interpret the results.The chapters conclude with several exercises based on data sets from different disciplines.

A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.

Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter. After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis. Features: Presents an accessible introduction to multivariate analysis for behavioral scientists Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage Includes nearly 100 exercises for course use or self-study Supplemented by a GitHub repository with all datasets and R code for the examples and exercises Theoretical details are separated from the main body of the text Suitable for anyone working in the behavioral sciences with a basic grasp of statistics

SPSS. Powerful, user-friendly, and useful, particularly in psychology, sociology, psychiatry, and other social sciences. But to fully exploit its capabilities and effectively execute its wide range of procedures, users need detailed, to-the-point guidance that other resources lack.

A Handbook of Statistical Analyses Using SPSS clearly and concisely describes how to conduct univariate and multivariate statistical analyses using the latest release of SPSS, version 11. Each chapter addresses a different type of analytical procedure and applies it to real data sets, primarily, though not exclusively, from the social and behavioral sciences. The authors briefly review the theoretical background of many of the techniques, but they set that material apart in boxes so that the presentation of the various analyses proceeds without interruption.

Each chapter ends with exercises, some related to the data sets introduced in that chapter, others introducing additional data sets. These data sets and answers to most of the exercises are available for download at www.iop.kcl.ac.uk/iop/departments/BioComp/SPSSBook.shtml.

If you're a relative newcomer to performing statistical analyses, a new user of SPSS, or a veteran on both fronts, A Handbook of Statistical Analyses Using SPSS will be a welcome addition to your library. Researchers and students alike will find this a convenient, self-contained route to using SPSS for effective data analysis.

With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, AHandbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language. Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The chapters conclude with several exercises based on data sets from different disciplines. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.

Updated to reflect SAS 9.2, A Handbook of Statistical Analyses using SAS, Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS. Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis. They demonstrate the analyses through real-world examples, including methadone maintenance treatment, the relation of cirrhosis deaths to alcohol consumption, a sociological study of children, heart transplant treatment, and crime rate determinants. With the data sets and SAS code available online, this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis. New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses Whether you're a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.

This dictionary provides over 2,000 brief but useful definitions of statistical terms frequently found in the medical and medical statistics literature. Where appropriate, the author illustrates terms with pictures or numerical examples, and minimizes the use of mathematical formulas. This book will be an essential reference for workers in all branches of medicine, applied statistics and biostatistics.

Since the first edition of this book was published, S-PLUS has evolved markedly with new methods of analysis, new graphical procedures, and a convenient graphical user interface (GUI). Today, S-PLUS is the statistical software of choice for many applied researchers in disciplines ranging from finance to medicine. Combining the command line language and GUI of S-PLUS now makes this book even more suitable for inexperienced users, students, and anyone without the time, patience, or background needed to wade through the many more advanced manuals and texts on the market.

The second edition of A Handbook of Statistical Analyses Using S-Plus has been completely revised to provide an outstanding introduction to the latest version of this powerful software system. Each chapter focuses on a particular statistical technique, applies it to one or more data sets, and shows how to generate the proposed analyses and graphics using S-PLUS. The author explains S-PLUS functions from both the Windows® and command-line perspectives and clearly demonstrates how to switch between the two.

This handbook provides the perfect vehicle for introducing the exciting possibilities S-PLUS, S-PLUS 2000, AND S-PLUS 6 hold for data analysis. All of the data sets used in the text, along with script files giving the command language used in each chapter, are available for download from the Internet at http://www.iop.kcl.ac.uk/iop/Departments/BioComp/splus.shtml