Book Description

Optimized for mobile devices, this handy resource provides diagnosis and treatment information on more than 500 medical disorders within a matter of seconds. Users can quickly select medical disorders via the Topic Guide Index or Table of Contents. ... Read more

Reviews (1)

Buy this book before designing a case-control study
If you are planning a case-control study, this book will be very useful, because it explains all steps,including sample size calculation and methods of analysis. ... Read more

Book Description

Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches.Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies. ... Read more

Reviews (1)

Outstanding
I had a chance to read this book cover to cover. All I can say is "absolutely outstanding", short of calling it a historical masterpiece in the field. Very rarely do I encounter an epidemiology or biostatistic textbook that reads so well. It is optimally reader friendly; the author appears to have such a talent in explaining some most sophisticated epidemiological and statistical concepts in such a simplified language. Yet he does not sacrifice the inclusion of some very advanced epidemiological and statistical concepts. New concepts such as causal graphs and instrumental variables are also included and explained beautifully. I strongly recommend this book to all early to intermediate graduate students majoring in Epidemiology. Established epidemiologists may wish to read this book to refresh and update their knowledge. I hope the author writes more textbooks with the same style. ... Read more

Book Description

Since the original publication of the bestselling Modelling Binary Data, a number of important methodological and computational developments have emerged, accompanied by the steady growth of statistical computing. Mixed models for binary data analysis and procedures that lead to an exact version of logistic regression form valuable additions to the statistician's toolbox, and author Dave Collett has fully updated his popular treatise to incorporate these important advances.Modelling Binary Data, Second Edition now provides an even more comprehensive and practical guide to statistical methods for analyzing binary data. Along with thorough revisions to the original material-now independent of any particular software package- it includes a new chapter introducing mixed models for binary data analysis and another on exact methods for modelling binary data. The author has also added material on modelling ordered categorical data and provides a summary of the leading software packages.All of the data sets used in the book are available for download from the Internet, and the appendices include additional data sets useful as exercises. ... Read more

Reviews (2)

Excellent Intermediate Level
Probably the best in its class, Dr. Collet's work is among those books that contain a great deal of information and clarity. It deals with model building with binary data. A vey good explanation on the rationale behind maximum likelihood aproach to parameter estimation, it also shows the application of the asimptotic properties of the most commonnly used models for cross tabulated data.
I hold a Ph.D. in agronomy and plant breeding, and this volume certainly gave me a good start for analyzing binary responses. Lots of examples help in understanding the theory. A 5 stars without any doubt.

an excellent self-learning guide to modelling binary models
This is an excellent volume on how one should tackle binary data. Whether you have previous experience on this type of data or not, one can learn a lot on the subject with this book.

It was written in an easily understood way so that one can really follow the examples and have a real go at one's own data while consulting the book.

As a former student of Prof. Collett I recall how clear his presentation as well as lecture notes had always been on even the most complicated subject he taught. I treaure this volume very much. ... Read more

Book Description

In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners.With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice. ... Read more

Reviews (1)

An good overview of the corps of the matter
This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods. ... Read more

Book Description

More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems.Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented. As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies. ... Read more

Reviews (1)

qol and missing data for statisticians
This is really the first really good book for statisticians on quality of life issues and methods with authoritative references. The author provides practical advice for qol studies in a clinical trials setting and provides sage advice on design of the study. Actual quality of life surveys are discussed and the importance of defining objectives is emphasized. Some real cancer trials are discussed and used as examples throughout the book. There is detailed treatment of missing data and how to deal with it including the latest statistical methodology. Much of the methodological development could apply to hard endpoints as well as qol endpoints.
I got this book and a good taste of its contents in a short course presented by the author at the Joint Statistical meetings in New York in August 2002. The only drawback of the book is that it does not spend much time on the issue of instrument validation. Also the author's experience is with cancer trials in an academic setting. The issues related to conducting qol surveys for a manufacturer that needs labeling or FDA approval efficacy and safety approvals is a prospective that I would like to see but the author does not have.
Excellent examples illustrated in SAS and SPlus. ... Read more

Book Description

Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail.Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices. ... Read more

Reviews (1)

Good introduction
A well-written introductory book. Broad range of material make it a good reference for new comers in survival analysis. ... Read more

Book Description

Field epidemiology involves the application of epidemiologic methods to unexpected health problems when a rapid on-site investigation is necessary for timely intervention.Based on decades of experience in both infectious and non-infectious diseases at the Centers for Disease Control and Prevention, this book describes in simple and practical terms the distinct approach, tasks, and actions needed for successful field investigations. Guidance is given on such issues as how to perform surveillance, manage and execute field investigations, collect and analyze data, perform surveys, adapt a personal computer for field use, and communicate the findings.Specific advice is also given on such subjects as dealing with the media; investigations in healthcare, day-care, and international settings; and the legal aspects of field studies.An entire chapter covers the proper collection, handling, and testing of infectious and non-infectious agents in the field.In the Second Edition, four new chapters cover many unique aspects of field studies in the workplace, after natural disasters, in preparation for and response to possible bioterrorist attacks, and by state and local health departments. Finally, an appendix describes how to investigatea common source food-borne epidemic.This text gives public health professionals and students a practical and complete reference to use in virtually any field investigation setting. ... Read more

Reviews (1)

Outstanding Resource for Epidemiologists & Researchers
"Field Epidemiology" by Gregg (editor) and colleagues from the U.S. federal government's Centers for Disease Control and Prevention is a vital resource for epidemiologists, health advocates, and public health researchers and specialists. The book is remarkable both for its content and the high quality of editorship, too rare a combination in biomedical texts.

The book's content addresses field epidemilogy, which is somewhat awkwardly defined as "..the application of epidemiology under the following set of general conditions: *The problem is unexpected. *An immediate response may be demanded. *Public health epidemiologists must travel to and work in the field to solve the problem. *The extent of the investigation is likely to be limited because of the imperative for timely intervention." Despite an awkward definition (i.e., defining field epidemiology by its antecedents), field epidemiology is the reality of responsive, responsible, comtemporary public health practice: A cluster of disease occurs of unknown origin, the public becomes concerned, public health agencies are expected to respond with compassion and utilitarian epidemiological methods. In this book's 18 chapters are described the hows, whys, and whats necessary to conduct field epidemiology. Each chapter is written by an expert in his or her area of epidemiology. A sample of chapter titles includes: Conducting a Field Investigation, Analyzing and Interpreting Data, Developing Interventions, Communicating Epidemiologic Findings, Dealing with the Public and Media, and Legal Considerations in a Field Investigation.

This book should also be savored for its clarity of expression, taut editorship, and integration of chapters. "Field Epidemiology" is an outstanding resource for any health professional's bookshelf. ... Read more

Book Description

From aspects of early trials to complex modeling problems, this useful reference summarizes current methodology used in the design and analysis of clinical trials. Chapters are contributed by internationally revered methodologists experienced in clinical trials practice. ... Read more

Book Description

Spatial Epidemiology is a rapidly growing field of research concerned with the analysis of the geographical distribution of disease. This principally involves mapping the location of disease cases and the analysis of the mapped data using spatial statistical methods. The growth of the field looks set to continue in line with increasing public, government and media concern about environmental and health issues.

• Comprehensive overview of the main statistical methods used in spatial epidemiology

• Contains many data examples - each represents a different approach to the analysis, and provides an insight into the various modelling techniques

• Describes modern simulation-based methods suitable for highly complex modelling problems

• Discusses the wide range of software available for analysing spatial data

• Contains an extensive bibliography

Reviews (1)

Great book very bad writing
This is a truly great book on this subject, but it is obvious that the text is mostly transcribed lecture or lab notes. The writing is so bad (run on and fragments) that the publisher should have caught this, and should provide all of us who purchased the book a free revised and updated copy.

But down to the real stuff. The author rightly critcizes the use of mapping rates by unit areas for to gain inference about disease patterns; and, provides some nifty insights into how important error modeling is; perhaps the book's real strong point. However, like most books on this subject, including the infamous Cressie, a huge assumption is made about the readers. Most public health people and epidemiologists have no clue about this area of analysis. Moreover, there needs to be a book where this stuff is presented so that most who would benefit could understand the subject matter.

If you like run on and sentence fragments and especially finding out what defines a real disease cluster then get this book. ... Read more

Book Description

Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models.Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application. Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling. ... Read more

Reviews (4)

the most clearly written book on the topic
My copy of the second edition just arrived yesterday and it is even better than the first edition (which was fantastic). The logical organization and clarity of writing make this book a 'must have' for any statistician's library. I'd give it 6 stars if I could. Readers should also check out McCulloch and Searle's 'Generalized, Linear and Mixed Models'.

Excellent concept - Execution could be better
I wish somebody would write a concise tutorial of the matematics required for an "intermediate" book such as Dobson's. Undoubtedly for someone whose acquaintence with modern statitical methods is more current this book is a gem. For someone like myself who wants a refresher and whose math is a bit rusty it leaves something to be desired. Some of the theoretical derivations in chapters 3 and 4 (keys to the understanding of the rest of the book) would be improved by a bit more detail and a thoroughly worked example. A major shortcoming is the lack of answers to the excercises; I don't see how the book was published without them. If the book was 100 pages longer with the addition of the aforementioned material, I would have given it a five star rating.

recommended for applications and clarity
...

Bill recommended Dobson's text because of her clear writing style and many useful examples. Dobson also places the theory in the context of the general exponential family of distributions. As I knew that the second edition was about to come out I waited for it.

The wait seems to have been very worthwhile. The second edition is a real bargin.... She has updated it with the many advances that have occurred over the past 12 years since the first edition was printed. This edition now includes some discussion of generalized additive models, broader coverage of applications as survival analysis, GEE, multi-level models and nominal and ordinal logistic regression have been added. It now offers the reader more applications in a wider variety of disciplines and includes modern approaches to diagnostic checking of the models.

As with the first edition, exploratory techniques are emphasized particularly graphical methods. The goal is to unify the apparently disparate statistical techniques that students are exposed to, into one general modeling framework.

It includes a nice up-to-date bibliography and recent advanced results on longitudinal models. The level is intermediate statistics with introductory statistics and linear models taken to be prerequisites. Students are also required to have some familiarity with calculus and linear algebra.

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This book provides a surprisingly brief and gentle, yet thorough, introduction to the subject of modeling dependent variables that are not continuous (see note below). The reader, who should be familiar with calculus-based probability, may initially find it frustrating that the actual practice of modeling nominal data is not discussed until the last two chapters (of 9). However, the cause for delaying the discussion of these models is to introduce the terminology and methodology of generalized linear models through more familiar linear regression models.

Thus, while this book is not ideal for someone who wants to jump right into the thick of building logistic, loglinear, or other models for nominal data, it is quite suitable for those wishing a thorough introduction to the practice of generalized linear modeling. For greater detail, a thicker book like McCullagh & Nelder's _Generalized Linear Models_ would be suitable.

Note: While the term "Generalized Linear Models" includes linear regression models (i.e., models for continuous dependent variables), reading this book is not the easiest way to be introduced to regression. A better starting point would be Draper & Smith's _Applied Regression Analysis_ or Weisberg's _Applied Linear Regression_. ... Read more

Book Description

For the new edition of Biostatistics and Epidemiology, Dr. Wassertheil-Smoller has included several new chapters (genetic statistics, molecular epidemiology, scientific integrity and research ethics) and a new appendix on the basic concepts of genetics and a glossary of genetic terminology. She has also expanded the coverage of multi-center trials (an important aspect of implementation of the standards of evidence-based medicine), controversies in screening for prostate, colon, breast, and other cancers. ... Read more

Book Description

Occupational epidemiology has emerged as a distinct subdiscipline of epidemiology and occupational medicine, addressing fundamental public health and scientific questions relating to the specification of exposure-response relationships, assessment of the adequacy of occupational exposure guidelines, and extrapolation of hazardous effects to other settings. This book reviews the wide range of principles and methods used in epidemiologic studies of working populations.It describes the historical development of occupational epidemiology, the approaches to characterizing workplace exposures, and the methods for designing and implementing epidemiologic studies.The relative strengths and limitations of different study designs are emphasized. Also included are more advanced discussions of statistical analysis, the estimation of doses to biological targets, and applications of the data derived from occupational epidemiology studies to disease modeling and risk assessment.The volume will serve both as a textbook in epidemiology and occupational medicine courses and as a practical handbook for the design, implementation, and interpretation of research in this field. ... Read more

Book Description

This book is a clinically oriented introduction to the new, emerging field of evidence-based medicine. ... Read more

Book Description

Statistical analysis is a powerful research tool. This user-friendly workbook provides a basic understanding of the nature and proper use of various statistical procedures. Written from the perspective of a biologist who uses statistics rather than from the standpoint of a statistician, Dr. Hampton's approach is much more applied than mathematical. Introductory Biological Statistics focuses on the commonly used parametric statistical tests, their nonparametric counterparts, and the conditions under which each test is appropriate or inappropriate. Dr. Hampton has included the more commonly used experimental designs and their associated computations, with some examples of each, to indicate their range of usefulness. He emphasizes the interrelatedness of design and analysis, as well as the assumptions upon which the various procedures are based. The assumptions of the tests are clear and accessible. "Caution Boxes" placed throughout the text help students avoid some of the more common pitfalls in using certain statistical procedures. In addition, the text offers exercises that use both small and large data sets, and answers to the exercises for chapters 3-9 are contained in an appendix. Key terms are boldfaced in the text and listed separately at the end of each chapter. ... Read more

Reviews (1)

Great Stats Intro!
If you're new to stats, or just keep forgetting the definition of chi squared, this is the book for you. Easy to read, good worked examples - both parametric and non-parametric (the autor will tell you what those are if you don't already know).
I use to borrow my roommates - now I'm buying my own as a handy reference.
NB. Does not go into a lot of detail of derivation. ... Read more

Book Description

This text on survival analysis provides a straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Throughout, there is an emphasis on presenting each new topic motivated with real examples of a survival analysis investigation, and then presenting thorough analyses of real data sets. Each chapter concludes with practice exercises to help readers reinforce their understanding of the concepts covered in the chapter. Readers can then extend their knowledge with a more thoroughgoing test. Answers to both are included. Beginning with the basic concepts of survival analysis-time to an event as a variable, censored data, and the hazard function-the author then introduces the Kaplan-Meier survival curves, the log-rank test, the Peto test, and the most widely used technique in survival analysis, the Cox proportional hazards model. Later chapters cover techniques for evaluating the proportional hazards assumptions, the stratified Cox procedure, and extending the Cox model to time-dependent variables. Readers will enjoy David Kleinbaum's style of presentation with numerous figures and diagrams illustrating each idea. As a result, this text makes an excellent introduction for all those coming to the subject for the first time. ... Read more

Reviews (3)

It's OK, but ...
Unlike other reviewers, I did not find this book very helpful, especially considering the price I paid. The book is essentially a PowerPoint course presentation published with the notes pages as text. Unfortunately, the book is laid out so that the reader must make the connection between the text and the slide itself (they're stacked side-by-side with no separation). Often, the text discusses the material as though the instructor had a pointer in hand to make the connection -- without those visual clues the argument is hard to follow. On the other hand, if you know nothing about survival analysis and only want to run computer programs (specifically SPIDA) and read the output, I guess this book isn't bad. I'll keep looking for a good textbook.

Survival Analysis and you....
I'm a graduate student in public health at Emory University and have had the opportunity to actually take the course in Epidemiologic Modeling with Dr. Kleinbaum. This book, as well as his self-learning text for logistic regression, are fabulous. Both books provide a good background to the methods needed to use each analytical technique. Survival Analysis: A Self-Learning Text, in particular, flows very well with good examples, diagrams, and explanations for the student who wishes to learn this technique. It also serves a great reference for those who use this analytical method.

Excellent Introduction to Survival Analysis
Kleinbaum's Survival Analysis: A Self-Learning Text is an excellent nontechnical introduction to survival analysis. Survival analysis are statistical techniques that addresses the problem of how much time it takes for an event to occur. The techniques is widely used in medical research, and my interest in it comes from wanting to explore how long it will take for a person to refinance a loan. Kleinbaum explores the topic in a straightforward, and easy-to-follow manner. The topics are illustrated through numerous figures, diagrams, and analysis of real data sets. Kleinbaum uses a minimial amount of mathematics and carefully leads the reader through any math that is used. The book concentrates on the Cox Proportional Hazard model which is the most widely used technique in survival analysis. Given the introductory nature of the book one will not find materials covering other models. Someone with some mathematical knowledge, one semester of calculus, and a semester of statistics and a semester of undergraduate econometrics would get the most out of this book. If you are looking for an introduction to survival analysis this is a great place to start. I feel I have a strong foundation to start using survival analysis at my job and continue with a more technical exploration. ... Read more

Book Description

Health & Numbers
A Problems-Based Introduction to Biostatistics
Second Edition
Chap T. Le, Ph.D.

Newly revised and updated, Health and Numbers, Second Edition serves as a concise introduction to basic statistical concepts and reasoning at a level suitable for a broad spectrum of students and professionals in medicine and allied health fields. This user-friendly resource uses real-life examples with real data to emphasize fundamental biostatistical concepts.

The book treats fundamental statistical methods with a practical, problems-based approach. It covers the nuts and bolts of elementary applied statistics for those who have no prior knowledge and experience in statistics. It provides an overview of basic ideas on standardization and rates and graphical techniques, offers more exercises than did the first edition, and features Microsoft® Excel and SAS samples, and new chapters on comparisons of means and proportions, as well as regression and correlation techniques. Health and Numbers, Second Edition strikes a balance between principles and methods of calculation that is particularly useful for students in medicine and health-related fields who are struggling with required courses in biostatistics. ... Read more

Book Description

An introduction to classical biostatistical methods in epidemiology
Biostatistical Methods in Epidemiology provides an introduction to a wide range of methods used to analyze epidemiologic data, with a focus on nonregression techniques. The text includes an extensive discussion of measurement issues in epidemiology, especially confounding. Maximum likelihood, Mantel-Haenszel, and weighted least squares methods are presented for the analysis of closed cohort and case-control data. Kaplan-Meier and Poisson methods are described for the analysis of censored survival data. A justification for using odds ratio methods in case-control studies is provided. Standardization of rates is discussed and the construction of ordinary, multiple decrement and cause-deleted life tables is outlined. Sample size formulas are given for a range of epidemiologic study designs. The text ends with a brief overview of logistic and Cox regression. Other highlights include:
* Many worked examples based on actual data
* Discussion of exact methods
* Recommendations for preferred methods
* Extensive appendices and references
Biostatistical Methods in Epidemiology provides an excellent introduction to the subject for students, while also serving as a comprehensive reference for epidemiologists and other health professionals.
For more information, visit www.wiley.com/mathematics ... Read more

Book Description

This book describes the recursive partitioning methodology and demonstrates its effectiveness as a response to the challenge of analyzing and interpreting multiple complex pathways to many illnesses, diseases, and ultimately death. For comparison purposes, standard regression methods are presented briefly and they are applied in the examples. We emphasize particularly the importance of scientific judgment and interpretation while guided by statistical output. This book is suitable for three broad groups of readers: 1) Biomedical researchers, clinicians, public health practitioners including epidemiologists, health service researchers, environmental policy advisers; 2) Consulting statisticians who can use the recursive partitioning technique as a guide in providing effective and insightful solutions to clients' problems; and 3) Statisticians interested in methodological and theoretical issues. The book provides an up-to-date summary of the methodological and theoretical underpinnings of recursive partitioning. It also presents a host of unsolved problems whose solutions whould advance the rigorous underpinnings of statistics in general. ... Read more

Reviews (3)

sequel to CART
Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning. There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.

Recursive Partitioning in the Health Sciences
Zhang and Singer have done a splendid job of explaining recursive partitioning, a topic that should be of great interest to anyone who wants to make sense of data in which there are many potentially important variables contributing to some outcome or variable of interest. One should not be put off by the "... in the Health Sciences" part of the book's title; the potential audience of readers who can benefit from reading it is much greater than this implies (I'm an ecologist, for example). Why? First, because the topics covered have wide applicability in many fields; and second, because the writing is exceptionally clear and easy to follow. If you are able to use a typical introductory text on multiple regression, for example, you should have no difficulty getting a lot out of Zhang and Singer. If you are able to handle a mathematically rigorous approach to statistics but are new to the topics covered here, this book will provide an excellent starting place before you jump into the many references to the recent literature provided by the authors.

Recursive Partitioning
Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic. ... Read more