Brian S. Everitt – författare

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.

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.

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.

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.

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 languag

At last – a new edition of the highly acclaimed book Clinical Trials in Psychiatry This book provides a concise but thorough overview of clinical trials in psychiatry, invaluable to those seeking solutions to numerous problems relating to design, methodology and analysis of such trials. Practical examples and applications are used to ground theory whenever possible. The Second Edition includes new information regarding: Recent important psychiatric trialsMore specific discussion of psychiatry in the USA and the particular problems of trials in the USA, including comments about the FDA (U.S. Food and Drug Administration)An extended chapter on meta-analysisFurther discussion of sub-group analysisSpecial features include appendices outlining how to design and report clinical trials, what websites and software programs are appropriate and an extensive reference section.From the reviews of the First Edition:“Everitt & Wessely are to be congratulated on producing an excellent guide to help overcome the snags in clinical trial research. Clearly written and in an engrossing style, the book is likely to become a classic textbook on clinical trials, and not just in psychiatry. The authors’ enthusiasm and grasp of clinical trial research make for a gripping and insightful read…it is one of the very best books that has been written on clinical trials.” THE BRITISH JOURNAL OF PSYCHIATRY"The experience of both authors in this area gives the book a very pragmatic approach grounded in reality, with theoretical overviews invariably being followed by practical examples and applications… an invaluable companion to anyone involved in, or contemplating undertaking, clinical trials research.” PSYCHOLOGICAL MEDICINE

Statistical methodology is of great importance to medical research and clinical practice. The Encyclopaedic Companion to Medical Statistics contains readable accounts of the key topics central to current research and practice. Each entry has been written by an individual chosen for both their expertise in the field and their ability to communicate statistical concepts successfully to medical researchers. Real examples from the biomedical literature and relevant illustrations feature in many entries and extensive cross–referencing signposts the reader to related entries.

Key Features:

Contains accounts of over 400 statistical topics central to current medical research. 80% of first edition entries updated and revised. Presents the latest techniques used at the cutting edge of medical research. Covers common errors in statistical analyses in medicine. Real examples from the biomedical literature and relevant illustrations feature throughout. Contains contributions from over 70 experts in the field.

Medical researchers, researchers and practitioners in medical research and statistics will benefit greatly from this book.

Statistical methodology is of great importance to medical research and clinical practice. The Encyclopaedic Companion to Medical Statistics contains readable accounts of the key topics central to current research and practice. Each entry has been written by an individual chosen for both their expertise in the field and their ability to communicate statistical concepts successfully to medical researchers. Real examples from the biomedical literature and relevant illustrations feature in many entries and extensive cross–referencing signposts the reader to related entries. Key Features: Contains accounts of over 400 statistical topics central to current medical research.80% of first edition entries updated and revised.Presents the latest techniques used at the cutting edge of medical research.Covers common errors in statistical analyses in medicine.Real examples from the biomedical literature and relevant illustrations feature throughout.Contains contributions from over 70 experts in the field.Medical researchers, researchers and practitioners in medical research and statistics will benefit greatly from this book.

During the last twenty years statistical methodology has become of central importance in research studies in medicine and also in day-to-day clinical practice. The medical literature is now liberally punctuated not only with relatively routine statistical terms such as p-value, t-test, confidence interval, and correlation, but also with more esoteric items such as hazard function, multilevel model, generalized estimating equations and crossover design. Consequently researchers in medicine and clinicians who are not primarily statisticians need to have a source that provides readable accounts of these terms so that they can understand at least the essence of the statistical aspects of both the design and analysis of a reported investigation. The Encyclopedic Companion to Medical Statistics is that source, containing readable accounts of over 500 statistical topics central to current medical research, with each entry being written by an expert in the field. Examples and graphical material supplement the written material in many entries, and extensive cross-referencing sign posts the reader to other entries that are likely to be relevant.

During the last twenty years statistical methodology has become of central importance in research studies in medicine and also in day-to-day clinical practice. The medical literature is now liberally punctuated not only with relatively routine statistical terms such as p-value, t-test, confidence interval, and correlation, but also with more esoteric items such as hazard function, multilevel model, generalized estimating equations and crossover design. Consequently researchers in medicine and clinicians who are not primarily statisticians need to have a source that provides readable accounts of these terms so that they can understand at least the essence of the statistical aspects of both the design and analysis of a reported investigation. The Encyclopedic Companion to Medical Statistics is that source, containing readable accounts of over 500 statistical topics central to current medical research, with each entry being written by an expert in the field. Examples and graphical material supplement the written material in many entries, and extensive cross-referencing sign posts the reader to other entries that are likely to be relevant.

Psychologists need to know about statistics but they can easily get bogged down in unfamiliar jargon. This new dictionary is an invaluable reference for all undergraduate and postgraduate students of psychology. It provides definitions of the statistical terms most commonly encountered in psychological literature. It explains simply and clearly what the methods/topics are and how to apply them, and in many cases, some mathematical details and a numerical example follow a brief written account of a term. In addition, graphical material help make definitions clearer. This unique approach makes this dictionary an essential purchase for all students grappling with increasingly sophisticated statistical methods in their data analysis.

Statistical science plays an increasingly important role in medical research. Over the last few decades, many new statistical methods have been developed which have particular relevance for medical researchers and, with the appropriate software now easily available, these techniques can be used almost routinely to great effect. These innovative methods include survival analysis, generalized additive models and Bayesian methods. Modern Medical Statistics covers these essential new techniques at an accessible technical level, its main focus being not on the theory but on the effective practical application of these methods in medical research. Modern Medical Statistics is an indispensable practical guide for medical researchers and medical statisticians as well as an ideal text for advanced courses in medical statistics and public health.

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.

At last – a new edition of the highly acclaimed book Clinical Trials in Psychiatry

This book provides a concise but thorough overview of clinical trials in psychiatry, invaluable to those seeking solutions to numerous problems relating to design, methodology and analysis of such trials. Practical examples and applications are used to ground theory whenever possible. The Second Edition includes new information regarding:

Recent important psychiatric trials More specific discussion of psychiatry in the USA and the particular problems of trials in the USA, including comments about the FDA (U.S. Food and Drug Administration) An extended chapter on meta-analysis Further discussion of sub-group analysis

Special features include appendices outlining how to design and report clinical trials, what websites and software programs are appropriate and an extensive reference section.

From the reviews of the First Edition:

“Everitt & Wessely are to be congratulated on producing an excellent guide to help overcome the snags in clinical trial research. Clearly written and in an engrossing style, the book is likely to become a classic textbook on clinical trials, and not just in psychiatry. The authors’ enthusiasm and grasp of clinical trial research make for a gripping and insightful read…it is one of the very best books that has been written on clinical trials.” THE BRITISH JOURNAL OF PSYCHIATRY

"The experience of both authors in this area gives the book a very pragmatic approach grounded in reality, with theoretical overviews invariably being followed by practical examples and applications… an invaluable companion to anyone involved in, or contemplating undertaking, clinical trials research.” PSYCHOLOGICAL MEDICINE

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.Key Features: Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysisProvides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li>Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured dataPractitioners and researchers working in cluster analysis and data analysis will benefit from this book.

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.

This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.

Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.

Key Features:

Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li> Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data

Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.

This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.

Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.

Key Features:

Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li> Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data

Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.

If you use statistics and need a useful, up-to-date and reliable sourcebook that provides simple definitions and explanations of statistical concepts, especially those used in biomedicine, then you will need this 1995 dictionary. Over 2000 terms are included here; Professor Everitt has aimed to provide brief but useful descriptions of those terms frequently found in the medical and medical statistics literature. Where appropriate, graphical illustrations or numerical examples are given, and the use of mathematical formulae has been minimised. This will be an essential reference for workers in all branches of medicine, applied statistics and biostatistics, especially research workers and research oriented clinicians.

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.

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

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, longitudi

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 mo

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

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;

For clinicians not well-versed in mathematical techniques, medical statistics can be baffling. Understanding these statistics is crucial for the interpretation of literature and the informed judgement of the use of therapies. From 'Abortion rate' to 'Zygosity determination', this accessible introduction to the terminology of medical statistics clearly describes, illustrates and explains over 1500 terms using non-technical language, and without any mathematical formulae! The majority of terms have been updated and revised for this new edition, and almost 150 new definitions have been added, ensuring readers are up to date with the latest practices. Entries are organised alphabetically, and related topics are clearly cross-referenced throughout, to provide fast, easy navigation. Further reading suggestions supplement most definitions, which allows readers to deepen their understanding of the subject. Enabling clinicians and medical students to grasp the meaning of any statistical terms they encounter when studying medical literature, this guide is a real lifesaver.

For clinicians not well-versed in mathematical techniques, medical statistics can be baffling. Understanding these statistics is crucial for the interpretation of literature and the informed judgement of the use of therapies. From ''Abortion rate'' to ''Zygosity determination'', this accessible introduction to the terminology of medical statistics clearly describes, illustrates and explains over 1500 terms using non-technical language, and without any mathematical formulae! The majority of terms have been updated and revised for this new edition, and almost 150 new definitions have been added, ensuring readers are up to date with the latest practices. Entries are organised alphabetically, and related topics are clearly cross-referenced throughout, to provide fast, easy navigation. Further reading suggestions supplement most definitions, which allows readers to deepen their understanding of the subject. Enabling clinicians and medical students to grasp the meaning of any statistical terms they encounter when studying medical literature, this guide is a real lifesaver.

For clinicians not well-versed in mathematical techniques, medical statistics can be baffling. Understanding these statistics is crucial for the interpretation of literature and the informed judgement of the use of therapies. From ''Abortion rate'' to ''Zygosity determination'', this accessible introduction to the terminology of medical statistics clearly describes, illustrates and explains over 1500 terms using non-technical language, and without any mathematical formulae! The majority of terms have been updated and revised for this new edition, and almost 150 new definitions have been added, ensuring readers are up to date with the latest practices. Entries are organised alphabetically, and related topics are clearly cross-referenced throughout, to provide fast, easy navigation. Further reading suggestions supplement most definitions, which allows readers to deepen their understanding of the subject. Enabling clinicians and medical students to grasp the meaning of any statistical terms they encounter when studying medical literature, this guide is a real lifesaver.

Statistical methodology is of great importance to medical research and clinical practice. The Encyclopaedic Companion to Medical Statistics contains readable accounts of the key topics central to current research and practice. Each entry has been written by an individual chosen for both their expertise in the field and their ability to communicate statistical concepts successfully to medical researchers. Real examples from the biomedical literature and relevant illustrations feature in many entries and extensive cross–referencing signposts the reader to related entries.

Key Features:

Contains accounts of over 400 statistical topics central to current medical research. 80% of first edition entries updated and revised. Presents the latest techniques used at the cutting edge of medical research. Covers common errors in statistical analyses in medicine. Real examples from the biomedical literature and relevant illustrations feature throughout. Contains contributions from over 70 experts in the field.

Medical researchers, researchers and practitioners in medical research and statistics will benefit greatly from this book.