Book Description

Computer Assisted Analysis of Mixtures summarizes developments in the field over the last 20 years . Along with advancements in theory and algorithms, it explores developments in biometric applications, such as meta-analysis, disease mapping, fertility studies, estimation of prevalence under clustering, and estimation of the distribution of survival times under interval-censoring. The approach is nonparametric for the mixing distribution, including leaving the number of components of the mixing distribution unknown. The text is particularly valuable to statisticians working with pharmaceutical clinical trials, epidemiologists, and social scientists. ... Read more

Book Description

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

This book provides an introduction to some of these new methods.The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes.The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.

The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.

Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.

Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.

Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).

Book Description

With the explosion of sequence data in public and private databases and the coming explosion of gene expression data in a similar vein, it is becoming increasingly important to understand how to apply well-established data analysis and data classification methods that have been developed in other fields to this field---to try to make sense of the data, to glean biological insights from it, to categorize the data, and to put all of these to good use in industrial applications.

This book introduces the main methods of data analysis and of data classification--as applied to sequence and gene expression analysis--to the biologist and to the computer scientist in this field. It contains material that is presently being taught by the author in the course Data Analysis, Modeling, and Visualization for Bioinformatics at the University of California, Santa Cruz Extension to workers in the biotechnology industry in Silicon Valley. ... Read more

Reviews (9)

This is like a lecture notes
This book has been used at UCSC extension, and seems most of the students are using this book like a lecture/summary notes, not as a full textbook. Without attending the class, it is VERY difficult to understand. However, once you attended the class (and study hard with some supplement textbooks if you are a beginner), you will realize that this book summarizes key points very well.
So... if you are a beginner to this field, you might want to look for an alternative. If you are already comfortable with statistics and bioinformatics, and want to check-out key points, maybe you are interested.

Not even close to Data Analysis or Classification
Do yourself a favour, don't buy this book. You can sit at home and search internet for bioinformatics and scratch with Microsoft word and you might end up with better notes.

Not worth it if it was half the price.
I bought the book for one of Dr. Jagota's classes at UCSC Extension and to say the least, I am very disappointed. The book is no more then an incomplete collection of his notes that he uses for one of his classes. There are very few complete examples and he uses terminology with out explanation, assuming the user knows everything about statistics and biology. This is not a good text for any one just starting out and I highly doubt it would be of much value to someone who is already in the field of Bioinformatics.

Not worth it at half the price.
I bought this book for the Data Analysis, Modeling, and Visualization class run by the author, Arun Jagota. It is little more then the notes he uses in his class bound in a nice little book. There are very few meaningful examples and the text assumes the reader already has a knowledge base in the subject since little detail is given in most areas of the book. If you are going to take this class and have to buy the book then I would suggest saving your money and buying the pdf file version of it and printing it out. Chances are you won't use this text very often.

Not good for students and beginners
Despite of an appearance of being mathematically sound, Jagota's book is full of hand-waving and imprecision. The author jumps from one topic to another using unexplained terminology (particularly in examples.) I also don't trust the author's attitude of commenting on the well established statistical terminology in a way that can confuse and mislead the reader. One example of such misleading comments is implying the existence of "symbolic random variable." In fact the "symbolic random variable" IS a "usual" random variable, as it should. But it is a minor problem comparing to the overall lack of precision, missing background explanations and often inadequate references.

In my judgement the book could be used by experienced bioinformaticians as a non-essential desk reference. Beginners and serious students should keep far away from it. ... Read more

Book Description

This volume presents the results of biological and medical research with the statistical methods used to obtain them. Nowadays the fields of biology and experimental medicine rely on techniques for the processing of experimental data and for the evaluation of hypotheses. Thus it is increasingly necessary to stimulate awareness of the importance, variety and flexibility of statistical techniques (and of the possible traps that they can hide) by using real data in concrete situations drawn from research activity, instead of hypothetical examples. The approaches taken to deal with the various topics in the book may seem disparate, and the heterogeneity is quite apparent. Nevertheless, although the authors talk about marine biology, physical anthropology, social medicine, population genetics, etcetera, many of the procedures can be traced back to the same fundamental and very common techniques, for example diagnosis and classification, clinical trials, Monte Carlo methods. The book opens with a critical discussion of the relationships between statisticians and researchers written by Professor D.V. Lindley.This volume can serve as a guide for young biologists and physicians who wish to become familiar with the most modern tools of statistical inference (for example non-parametric discriminant analysis, hierarchical models and related computational techniques).Audience: This volume is suitable for researchers, teachers and students in applied statistics, medicine, genetics, biology, ethology and physical anthropology. ... Read more

Book Description

* 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. ... Read more

Book Description

Written for physicians, medical students, and other health care professionals, this concise textbook explains epidemiologic concepts clearly and simply. It bridges the gap between the clinician and the epidemiologist by providing a number of clinical examples, and explains the epidemiologic emphasis on the study of groups rather than individuals.

You'll also find examination ofthe relationship between epidemiology and the study of social and political problems such the changing health care systems and environmental hazards. ... Read more

Book Description

This text presents epidemiologic methods for studying injuries and evaluating interventions to prevent them to students and health care and safety professionals. Injuries can be classified under many descriptions, such as causal energy, agent of energy impact, and behavioral interventions. The formations of research questions and the choice of research methods may reveal or obscure patterns which can lead to remarkable reductions in injury. Relatively simple descriptive studies are helpful in targeting and improving injury control programs, while changeable factors are revealed only by more sophisticated analytic methods. The sources for such reliable, valid data and exemplary study designs are contained in this essential textbook. In addition, the difficulties in using rates and ratios, and applying epidemiologic methods when evaluating programs, laws, and regulations are covered. The use of economic concepts and policy analysis, topics not usually found in epidemiology texts, is also discussed and illustrated. ... Read more

Book Description

Featuring articles from the prestigious Encyclopedia of Biostatistics, many of which have been revised and updated to include recent developments, the Encyclopedia of Epidemiologic Methods also includes newly commissioned articles reflecting the latest thinking in

• Cancer Registries
  
• Birth Defect Registries
  
• Meta Analysis of Epidemiologic Studies
  
• Epidemiology Overview
  
• Sample Size
  
• Sex Ratio at Birth
  
• Software Design and Analysis

Featuring contributions from leading experts in academia, government and industry, the Encyclopedia of Epidemiologic Methods has been designed to complement existing texts on the subject by providing further extensive, up-to-date coverage of specialised topics and by introducing the reader to the research literature.

Offering a wealth of information in a single resource, the Encyclopedia of Epidemiologic Methods

• Offers an excellent introduction to a vast array of specialised topics
  
• Includes in-depth coverage of the statistical underpinnings of contemporary epidemiologic methods
  
• Provides concise definitions and introductions to numerous concepts found in the current literature
  
• Uses extensive cross-references, helping to facilitate further research, and enabling the reader to locate definitions and related concepts

In addition to featuring extensive articles in the areas of descriptive and analytic epidemiology, the Encyclopedia also provides the reader with articles on case-control design and offers substantial coverage of allied statistical methods.

From the Reviews….
"a selection of excellent articles"
(Short Book Reviews, December 2001)

"The articles were written by experts...hence the quality of entries is quite good. The editors have done an excellent job..."
(Journal of the American Statistical Association, December 2001)

"There is no doubt that this…is an invaluable tool for researchers involved in medical and public health research as it offers an excellent springboard for acquiring and strengthening practical and methodological tools."
(Short Book Reviews, Vol. 21, No. 3, December 2001)

"...illuminating and comprehensive and often of extremely high quality..."
(Biometrics, September 2002)

... Read more

Reviews (2)

compilation of authoritative articles
For a biostatistician at a pharmaceutical company who needs to combine clinical trial data with information in the literature from case-control and other non-randomized data, understanding of the latest epidemiologic methods is essential. In this reference source Mitch Gail and Jacques Benichou provide all the important abbreviations and acronyms and 201 articles listed alphabetical by title along with author and subject indices at the end of the volume. Each article has its own list of references to the relevant literature. The list of contributors is over 140 including many very famous biostatisticians from around the world.

It is a single volume that is 978 pages long and although it is expensive for an individual to purchase, it merits having in a biostatistics library or any technical library where medical research is conducted.

Here is a selected list of topics: Age-Period-Cohort Analysis, Berkson's Fallacy, Bias, Bias in Obersvational Studies, Case-Cohort Study, Case-Control Study, Causation, Cornfield's Inequality, Cox Regression Model, Death Indexes, Demography, Dose-Response, Epidemiology as Legal Evidence, Frequency Matching,Hawthorne Effect, Kappa, Length Bias, Life Table, Logistic Regression, Meta-Analysis in Epidemiology, Occupational Mortality, Odds Ratio, Prevalence, Propensity Score,Retrospective Study, Selection Bias, Simpson's Paradox, and Validation Study.

Among the contributors are Peter Armitage, Norman Breslow, Ray Carroll, Ted Colton, Mitch Gail, Joe Gastwirth, David Hosmer, Gary Koch, Stanley Lemeshow, Rod Little, Ross Prentice, George Seber, Butch Tsiatis, and Michael Vaeth. The work is very much up-to-date as it was published in 2000. It includes updates of many articles that were in the highly praised six volume Encyclopedia of Biostatistics. It concentrates on those articles that focus on topics important to epidemiology. It also includes several articles written specifically for this volume.

Young scientist
This is a very valuable book, helping to be familiar with epidemiological methods. This will alllow you to use right epidemiological methods in preparing scientistic work. Thank for authors. ... Read more

Book Description

Equivalence testing has grown significantly in importance over the last two decades, especially as its relevance to a variety of applications has become understood. Yet published work on the general methodology remains scattered in specialists' journals, and for the most part, it focuses on the relatively narrow topic of bioequivalence assessment.With a far broader perspective, Testing Statistical Hypotheses of Equivalence provides the first comprehensive treatment of statistical equivalence testing. The author addresses a spectrum of specific, two-sided equivalence testing problems, from the one-sample problem with normally distributed observations of fixed known variance to problems involving several samples and multivariate data. The treatment includes a concise review of basic mathematical results on optimal tests for equivalence, and the author makes available on the Internet a collection of computer programs that allow easy implementation of the methods presented.In a field as complex and rich in potential applications as equivalence testing, Testing Statistical Hypotheses of Equivalence stands alone as a coherent reference that furnishes both the theoretical and practical tools needed for dealing with equivalence trials of any complexity and in any phase. ... Read more

Book Description

Features 21 case studies that illustrate commonly used approaches to answer scientific questions in such areas as biology, toxicology, clinical medicine, environmental hazards, agriculture, forestry and wildlife. Examples of statistical methods used in these case studies include linear regression, survival analysis, principle components, design of experiments, resampling and bootstrap. A disk containing the collective data sets will accompany the book. ... Read more

Reviews (1)

great collection of case studies to learn from
These well known editors have compiled a collection of 21 case studies written by very accomplished statisticians and biostatisticians. It covers many different topics and provides insights and examples to learn from.

It is divided into six topical areas. The first three papers deal with environmental hazards. The next five are characterized as being in forestry, fisheries or genetics. Part three consists of three articles in the category of habitat and animal studies. Part 4 deals with health care and public policy and includes two articles. Part 5 is four articles on clinical trials. Part 6 contains the remaining four articles that deal with epidemiology and toxicology.

Most chapters have multiple authors. There are 53 authors covering the 21 articles. Many well known statisticians are contributors. The book also includes a data disk with examples from the case studies. ... Read more

Book Description

The basis for much of medical public health practice comes from epidemiological research.This text describes current statistical tools that are used to analyze the association between possible risk factors and the actual risk of disease.Beginning with a broad conceptual framework on the disease process, it describes commonly used techniques for analyzing proportions and disease rates.These are then extended to model fitting, and the common threads of logic that bind the two analytic startegies together are revealed. Each chapter provides a descriptive rationale for the method, a worked example using data from a published study, and an exercise that allows the reader to practice the technique.Each chapter also includes an appendix that provides furhter details on the theorheticalunderpinnings of the method. Among the topics covered are Mantel-Haenszel methods, rates, survival analysis, logistic regression, and generalized linear models.Methods for incorporating aspects of study design, such as matching, into the analysis are discussed, and guidance is given for determining the power or the sample size requirements of a study.This text will give readers a foundation in applied statistics and the concepts of model fitting to develop skills in the analysis of epidemiological data. ... Read more

Book Description

Investigating Biological Systems Using Modeling describes how to apply software to analyze and interpret data from biological systems. It is written for students and investigators in lay person's terms, and will be a useful reference book and textbook on mathematical modeling in the design and interpretation of kinetic studies of biological systems. It describes the mathematical techniques of modeling and kinetic theory, and focuses on practical examples of analyzing data. The book also uses examples from the fields of physiology, biochemistry, nutrition, agriculture, pharmacology, and medicine.

Key Features:
* Contains practical descriptions of how to analyze kinetic data
* Provides examples of how to develop and use models
* Describes several software packages including SAAM/CONSAM
* Includes software with working models ... Read more

Book Description

The first edition of Design and Analysis of Cross-Over Trials quickly became the standard reference on the subject and has remained so for more than 12 years. In that time, however, the use of cross-over trials has grown rapidly, particularly in the pharmaceutical arena, and researchers have made a number of advances in both the theory and methods applicable to these trials.Completely revised and updated, the long-awaited second edition of this classic text retains its predecessor's careful balance of theory and practice while incorporating new approaches, more data sets, and a broader scope. Enhancements in the second edition include:·A new chapter on bioequivalence·Recently developed methods for analyzing longitudinal continuous and categorical data·Real-world examples using the SAS system·A comprehensive catalog of designs, datasets, and SAS programs available on a companion Web site.The authors' exposition gives a clear, unified account of the design and analysis of cross-over trials from a statistical perspective along with their methodological underpinnings. With SAS programs and a thorough treatment of design issues, Design and Analysis of Cross-Over Trials, Second Edition sets a new standard for texts in this area and undoubtedly will be of direct practical value for years to come. ... Read more

Book Description

This book presents new and powerful advanced statistical methods that have been used in modern medicine, drug development, and epidemiology. Some of these methods were initially developed for tackling medical problems.

All 29 chapters are self-contained. Each chapter represents the new development and future research topics for a medical or statistical branch. For the benefit of readers with different statistical background, each chapter follows a similar style: the explanation of medical challenges, statistical ideas and strategies, statistical methods and techniques, mathematical remarks and background and reference. All chapters are written by experts of the respective topics. ... Read more

Book Description

This book combines applied and theoretical approaches to the analysis of epidemiologic issues.It goes beyond elementary material to deal with real problems generated by disease data, and delves into less usual areas such as the analysis of spatial distributions, survival data, proportional hazards regression, and "computer-intensive" approaches to statistical estimation.Each methods discussed in the text is illustrated with examples which include complete sets of data.Using actual data demonstrates the strengths and weaknesses of different analytic approaches in describing a disease process.The goal of the book is to allow the reader to develop a clear understanding of analytic approaches to problems in epidemiologic data analysis without relying on sophisticated mathematics and advanced statistical theory. For the Second Edition a new chapter on the analysis of matched data has been added.This covers both discrete and continuous outcomes and explains both the classic analytic approach and the conditional logistic regression model.New sections have also been added on contingency table data, misclassification, and additive models underlying tabular data.In all the chapters there are new applications and other revisions that make this Second Edition a clearer and more helpful exposition of the way statistical tools are used to analyze epidemiologic data. ... Read more

Book Description

If something can fail, it can often fail in one of several ways and sometimes in more than one way at a time. There is always some cause of failure, and almost always, more than one possible cause. In one sense, then, survival analysis is a lost cause. The methods of Competing Risks have often been neglected in the survival analysis literature. Written by a leading statistician, Classical Competing Risks thoroughly examines the probability framework and statistical analysis of data of Competing Risks. The author explores both the theory of the subject and the practicalities of fitting the models to data. In a coherent, self-contained, and sequential account, the treatment moves from the bare bones of the Competing Risks setup and the associated likelihood functions through survival analysis using hazard functions. It examines discrete failure times and the difficulties of identifiability, and concludes with an introduction to the counting-process approach and the associated martingale theory.With a dearth of modern treatments on the subject and the importance of its methods, this book fills a long-standing gap in the literature with a carefully organized exposition, real data sets, numerous examples, and clear, readable prose. If you work with lifetime data, Classical Competing Risks presents a modern, comprehensive overview of the methodology and theory you need. ... Read more

Reviews (1)

first good text on this since David and Moeschberger in 1978
Crowder has written an up-to-date text on an important problem in health science and medical research. The first good monograph on this subject was by David and Moeschberger in 1978 and no text devoted to this topic had been written until now. People get sick or die or hardware fails due to one of several possible causes. In the competing risks model these causes are all given probabilities of occurrence over time and "compete" to be the first to occur and thus cause the event.

The mathematics of competing risks is very much the same as the mathematics of survival analysis but instead of a single time to event curve there are many. For data analysis, one must be able to get data that includes not only the time of occurrence of the failure but also which of a list of possible causes the event is attributed to (the list of "competing" risks).

Crowder's text is introductory and reviews a lot of the basics of survival analysis and likelihood inference. Hazard functions and survival curves are introduced as are sub-survival curves and sub-hazard functions. The nonparametric Kaplan-Meier approach to survival analysis is presented as is the semiparametric Cox proportional hazards model. The important issue of parameter identifiability is given its proper place of importance.

The first seven chapters are written at an elementary to intermediate level that should be understandable to the undergraduate or graduate student taking this course. However, Chapter 8 deals with the modern and powerful counting process (martingale) approach to survival analysis and is more difficult to read. Chapter 8 has more of the flavor of an advanced probability topic and is suitable for graduate students who have taken that first advanced probability course. ... Read more