Book Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered."
Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book"
Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally."
Journal of the American Statistical Association, March 2006
Customer Reviews:
very good book, compact but comprehensive.......2007-05-12
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
Excellent book ..........2007-01-09
A very specific book, with a lot of details for a statistitian
Book Description
A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.
The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.
Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include:
* Variable selection.
* Identification of the scale of continuous covariates.
* The role of interactions in the model.
* Interpretation of a fitted model.
* Assessment of fit and model assumptions.
* Regression diagnostics.
* Recurrent event models, frailty models, and additive models.
* Commercially available statistical software and getting the most out of it.
Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.
Customer Reviews:
A Good Read, but Read it Carefully!.......2005-05-05
The authors provide a really nice, non-technical survey of the landscape for Cox Proportional Hazards models. A nice aspect of their treatment is the care they take to reference all highly technical texts and journal articles. For example, if you'd like to find out more about goodness-of-fit tests for survival models, the authors provide ample references to the Counting Process Theory of Martingale Residuals.
The first chapter discusses the basic characteristics of survival data, including the notion of censoring (in all of its various forms). Examples of the principle types of censoring are included. The chapter also includes introductory material on the general survival model, including a nice description of the log likelihood function. Curiously, the rigorous definition of the hazard function has been omitted, probably to avoid intimidating readers who are not familiar with formal limits.
Chapter 2 continues to build up the general survival model and introduces the relationship between the survivor function and the cumulative hazard. Pointwise estimators for the survivor function are discussed, including the Kaplan-Meier estimator along with the various variance estimators. Test statistics for comparing two survival populations are introduced, including the Log-Rank and General Wilcoxon statistics. The reader is encouraged to read the counting process treatments of these statistics to see why they produced defensible hypothesis tests.
Chapter 3 is devoted to the Cox Model and Cox's partial likelihood function. Tests for significance of the coefficients are introduced, included the Wald test, log likelihood ratio test and the score test. These are used heavily in the later chapters as the basis of a model-building methodology.
Chapter 4 is a very short, but nicely written chapter explaining how to interpret the values of each regression coefficent. It also describes covariate-adjustment techniques for model diagnostics.
Chapter 5 is just a wonderful chapter which outlines classical model building techniques. This is a great chapter for anyone who has ever been thrown a ton of data (with a bushel of possible covariates) and asked to "fit a model to this stuff".
Readers who have done a lot of purposeful fitting of linear regression models won't find the basic techniques new, but use of survival specific residuals and selection criterion will probably be an eye-opener. The section on assessing the functional form for continuous covariates is also nicely written.
However, the section on Best Subsets Selection was a little too "cook-booky" for my taste.
Chapter 6 is another very nice chapter on goodness-of-fit. It discusses analysis of the various residuals and their use for analysis outliers, testing proportional hazards assumptions and overall Goodness-of-Fit.
Chapter 7 discusses the standard extensions of the Cox model, including stratification and time-varying covariates. Chapter 8 discusses parametric survival models, and is a good introduction to the SAS procedure LIFEREG. The generalization of the Cox model to recurring event data (also know as Aalen's multiplicative intensity model) can be found in Chapter 9.
My only complaint is that each chapter was designed to be read in one sitting. Individual ideas, topics and formulas can be buried in a seemingly unbroken chain of paragraphs. The lack of sub-sub section titles,etc, makes using the text as is somewhat cumbersome to use as a desk reference. I've gotten around this limitation by marking key concepts, etc., in the margin in order to give a "quick search" capability enhancement to the index.
nice introduction.......2003-04-03
This book provides a good, clear, concise explanation of Cox's proportional hazards models. For someone seeking a non-mathematical description this is a great guide. The original datasets from the text examples can even be downloaded and you can go through the same process yourself. Because of some mistakes in the text, I would recomend looking at other sources as well.
Great conceptual Introduction to Cox regression analysis.......2000-02-09
I enjoyed the authors' book on logistic regression analysis in 1989, and this book is just as good, or better, with many extremely practical suggestions on building regression models for survival data. Happily, the authors summarize, compare, and contrast several major texts on survival analysis which have appeared in the past 10 years. For example, they discuss different names used by different authors for score residuals. They present a helpful appendix on the counting process approach to survival analysis, which will make more advanced texts accessible to students; thus, anyone who wants to use survival analysis, at any level, should consult this book, even if he has already studied books by Miller, Lee, Collett, Fleming-Harington,Andersen, et al, etc. An unfortunate drawback to this book is that the first printing contains many careless errors, some of which may affect student learning: for example, the definition of a survival function is misstated. I recommend that you insist on the second or third printing when buying this book, and you will be quite satisfied.
A clear, simple introduction to survival models.......2000-01-07
Hosmer and Lemeshow have given us a clear, nontechnical introduction to using survival models. The book strikes a good balance between covering the basics and addressing the most recent, state-of-the-art techniques, including repeated events, frailty models, and others. They also do a good job of addressing practical issues, including estimation details and available software. While most of the examples are drawn from medicine and biostatistics, this book could also serve as a useful starting point for social and behavioral scientists interesting in learning the fundamentals of these models, as well as a useful reference for applied researchers.
Excellent Nontechnical Coverage of Survival Analysis.......1999-12-07
Applied Survival Analysis is an excellent book for someone seeking a non-mathematicial explanation of survival analysis. The book covers the motivation behind the development of survival analysis, estimation of survival curves, the Cox proportionial hazards, and some parametric models. The book also covers the major methods used in variable selection, model building, and diagnostics. Someone with an undergraduate background in statistics and econometrics will understand the book. The book relies on text to discuss the methods and uses mathematical formulas only when absolutely necessary. Numerous examples are used to highlight what the text covers. The math that is used is easily understandable. This book is ideal for someone who needs to learn the tools of survival analysis but not how they were derived.
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. All of the data sets used in the book are available for download from www.crcpress.com/e_products/downloads. 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.
Customer Reviews:
Good introduction.......2000-03-30
A well-written introductory book. Broad range of material make it a good reference for new comers in survival analysis.
Book Description
Applied statisticians in many fields frequently analyze time-to-event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring and truncation. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex techniques accessible to applied researchers without the advanced mathematical background. The authors present the essentials of these techniques, as well as classical techniques not based on counting processes, and apply them to data. The second edition contains some new material as well as solutions to the odd-numbered revised exercises. New material consists of a discussion of summary statistics for competing risks probabilities in Chapter 2 and the estimation process for these probabilities in Chapter 4. A new section on tests of the equality of survival curves at a fixed point in time is added in Chapter 7. In Chapter 8 an expanded discussion is presented on how to code covariates and a new section on discretizing a continuous covariate is added. A new section on Lin and Ying's additive hazards regression model is presented in Chapter 10. We now proceed to a general discussion of the usefulness of this book incorporating the new material with that of the first edition.
Customer Reviews:
Good Book.......2007-02-11
I am a computer scientist and using this book for my research to address a problem. This book is well written but of course target audience are people with solid background in probability theory and parameteric estimation (pattern recognition). Therefore please do not expect that author will teach you basic probability theory. Contents are more applied in nature therefore natural audience are staticians and researchers.
Long-winded and uninformative.......2006-12-15
This textbook is too heavy on mathematical theory, and as a result ends up being largely uninformative. It is also long-winded to the point of being interminable. In order to implement survival analysis techniques, the practicing statistician does not need to wade through endless proofs, derivations, and digressions into the specification of likelihood functions. There are many textbooks available that provide a more intuitive understanding of survival analysis techniques, in a much shorter space.
Good book of Studying Survival Analysis.......2004-02-15
In this new edition, most of the errata are corrected and the texts are explained in a more detailed way.
The formulae are correct and the examples are explained in a more direct and expressive way than that in the 1st edition.
The most valuable one is its Theoretical Notes and Practical Notes. They show a lot of different points of views.
A good-buy and must-read for those want to have an intense level in Survival Analysis. Suitable for elementary and intermediate candidates to read and study.
Ian Lauder
A good book of studying Survival Analysis.......2004-02-15
In this new edition, many errata are corrected and each of the theories has a reason why this is true. Although they may be more practical in view, it is very good to use them to learn Survival Analysis, which is more realistic in sense.
In its Theoretical Notes and Practical Notes, there are a lot of different views and sights to show that which is the best to use. The examples are more or less good one and explained in a more detailed way than that in the 1st edition. A good-buy and must-read for those want to have a thorough view in this aspects. Read them carefully! Better than Cox's in this new edition. Buy and read this new edition!
Good for practitioners, but not for statistician.......2002-12-10
This book describes formulas and list of applications, but it don't give more accurate statistical reasons. I get how to use the formulas, but i think i am more interested in how to get the formulas
Book Description
A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.
The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.
Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include:
* Variable selection.
* Identification of the scale of continuous covariates.
* The role of interactions in the model.
* Interpretation of a fitted model.
* Assessment of fit and model assumptions.
* Regression diagnostics.
* Recurrent event models, frailty models, and additive models.
* Commercially available statistical software and getting the most out of it.
Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.
Average customer rating:
- Clarity at last!
- useful book
- The only survival analysis book you'll ever need
- It's OK, but ...
- Survival Analysis and you....
|
Survival Analysis: A Self-Learning Text (Statistics for Biology and Health)
David Kleinbaum , and
Mitchel Klein
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
 General
| Science
| Subjects
| Books
General
| Applied
| Mathematics
| Science
| Subjects
| Books
Probability & Statistics
| Applied
| Mathematics
| Science
| Subjects
| Books
General
| Mathematics
| Science
| Subjects
| Books
Basic Science
| Medicine
| Subjects
| Books
| Anatomy
| Biochemistry
| Embryology
| General
| Genetics
| Histology
| Immunology
| Microbiology
| Nosology
| Pathophysiology
| Physiology
General
| Medicine
| Subjects
| Books
Biostatistics
| Research
| Medicine
| Subjects
| Books
Biostatistics
| Biological Sciences
| Professional Science
| Professional & Technical
| Subjects
| Books
General
| Applied
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
Statistics
| Applied
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
Statistics
| Mathematics
| Sciences
| New & Used Textbooks
| Stores
| Books
General
| Mathematics
| Sciences
| New & Used Textbooks
| Stores
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Medicine
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Professional
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Science
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Similar Items:
-
Logistic Regression (2nd Edition)
-
Survival Analysis Using SAS: A Practical Guide
-
Survival Analysis
-
Epidemiology: Beyond the Basics
-
Applied logistic regression (Wiley Series in probability and statistics)
ASIN: 0387945431 |
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.
Customer Reviews:
Clarity at last!.......2006-08-31
I'm ABD Economics and down to the dissertation. I have 10 titles on my shelf that deal directly with survival/event-history analysis. I've plowed though them all. Finally (!) I have one that is useful; and, this is the one. If you are not already familiar with this method and/or you are only going to get one book - this is the one to acquire. Far and away it beats everything else I've purchased. Don't be put off the by epidemiological examples - they're easy enough to read through. The authors' personal preference seems to be for STATA, but SAS and SPSS code are available in the appendix.
useful book .......2006-03-06
this is a very useful book to introduce you to the concepts of survival analysis. It is better for those who already have basic knoweledge abour regression models but it can be used by beginners as well. Basic knowledge of statistics is strongly required.
The only survival analysis book you'll ever need.......2005-09-29
A very well written, step by step book on survival analysis, recommended for all, absolute beginners as well as experienced
biostatisticians.
The authors truly deserve praise.
It's OK, but ..........2003-07-24
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...........1999-12-16
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.
Average customer rating:
|
Applied Survival Analysis (Wiley Series in Probability and Statistics)
Chap T. Le
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Paperback
General
| Biology
| Biological Sciences
| Science
| Subjects
| Books
General
| Science
| Subjects
| Books
Probability & Statistics
| Applied
| Mathematics
| Science
| Subjects
| Books
Basic Science
| Medicine
| Subjects
| Books
| Anatomy
| Biochemistry
| Embryology
| General
| Genetics
| Histology
| Immunology
| Microbiology
| Nosology
| Pathophysiology
| Physiology
General
| Medicine
| Subjects
| Books
General
| Research
| Medicine
| Subjects
| Books
Epidemiology
| Infectious Disease
| Internal Medicine
| Medicine
| Subjects
| Books
General
| Biology
| Biological Sciences
| Professional Science
| Professional & Technical
| Subjects
| Books
Biostatistics
| Biological Sciences
| Professional Science
| Professional & Technical
| Subjects
| Books
Statistics
| Applied
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
Research
| Medical
| Professional & Technical
| Subjects
| Books
General
| Medical
| Professional & Technical
| Subjects
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Medicine
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Professional
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Science
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Similar Items:
-
Survival Analysis
ASIN: 0471170852 |
Book Description
This concise, application-oriented text is designed to meet the needs of practitioners and students in applied fields in its coverage of major, updated methods in the analysis of survival data. Includes analysis of standardized mortality ratios, methods for proving attenuation of healthy worker effects, ordinal risk factors and other new areas of research. Timely and diverse case studies are presented, plus a complete data set on ESRD patients on hemodialysis. Moderate level of mathematics required.
Book Description
Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. Applications where such data appear are survival of twins, survival of married couples and families, time to failure of right and left kidney for diabetic patients, life history data with time to outbreak of disease, complications and death, recurrent episodes of diseases and cross-over studies with time responses. As the field is rather new, the concepts and the possible types of data are described in detail and basic aspects of how dependence can appear in such data is discussed. Four different approaches to the analysis of such data are presented. The multi-state models where a life history is described as the subject moving from state to state is the most classical approach. The Markov models make up an important special case, but it is also described how easily more general models are set up and analyzed. Frailty models, which are random effects models for survival data, made a second approach, extending from the most simple shared frailty models, which are considered in detail, to models with more complicated dependence structures over individuals or over time. Marginal modelling has become a popular approach to evaluate the effect of explanatory factors in the presence of dependence, but without having specified a statistical model for the dependence. Finally, the completely non-parametric approach to bivariate censored survival data is described. This book is aimed at investigators who need to analyze multivariate survival data, but due to its focus on the concepts and the modelling aspects, it is also useful for persons interested in such data, but not having a statistical education. It can be used as a textbook for a graduate course in multivariate survival data. It is made from an applied point of view and covers all essential aspects of applying multivariate survival models. Also more theoretical evaluations, like asymptotic theory, are described, but only to the extent useful in applications and for understanding the models. For reading the book, it is useful, but not necessary, to have an understanding of univariate survival data. Philip Hougaard is a statistician at the pharmaceutical company Novo Nordisk. He has a Ph.D. in nonlinear regression models and is Doctor of Science based on a thesis on frailty models. He is associate editor of Biometrics and Lifetime Data Analysis. He has published over 80 papers in the statistical and medical literature.
Customer Reviews:
first book on multivariate survival analysis.......2001-01-03
The author is one of the pioneers in the newly developing field of multivariate survival analysis. His work goes back to his Ph.D. dissertation in the mid 1980s. These methods come into play when one is studying more than one survival curve and the event times are correlated rather than independent. Practical applications include situations when multiple events are studied on the same patients, such as time until contracting the disease, followed by time to complications and then possibly by time to death from the disease. Studies involving events related to twins can also be analyzed by these methods.
He gives an excellent exposition and a number of good examples. He provides the reader with a very current list of references from the literature.
The author presents the four common approaches to the problem and concedes that the field is in its infancy. He believes that while some of the methods described will prove not to be as fruitful as others, at this point it is still difficult to determine which are the most promising. His aim is to expand the toolbox for researchers in medical and biological fields who have experience with univariate survival analysis and may be faced with multivariate problems. He covers such important current topics as fraility models and competing risks.
In my opinion the author has succeeded in his goal and provided biostatisticians with a reference source that will be useful to them for many years. It should not be your first book in survival analysis though. See the book by Lawless or Kalbfleish and Prentice before attaching this book.
Books:
- Renzo Piano
- Rescuing Science from Politics: Regulation and the Distortion of Scientific Research
- Rockwood and Wilkins' Fractures in Children: Rockwood, Green, and Wilkins' Fractures (Not Sold as a Volume Set)
- Rulemaking: How Government Agencies Write Law and Make Policy (Rulemaking: How Government Agencies Write Law & Make Policy)
- Sax's Dangerous Properties of Industrial Materials (3 Volume Set)
- Spaces VI: A Compemporary Vision (Spaces (Bilingual))
- Spanish for Health Care Professionals
- Stealing the Network: How to Own a Shadow (Stealing the Network) (Stealing the Network)
- Stop Sitting on Your Assets: How to Safely Leverage the Equity Trapped in Your Home and Transform It Into a Constant Flow of Wealth and Security
- Supervision of Police Personnel (6th Edition)
Books Index
Books Home
Recommended Books
- Hot Gimmick, Vol. 1
- Gypsies: The Hidden Americans
- An Introduction Chemistry Of The Silicones
- Bread Givers: A Novel
- Casino Operations Management
- Environmental and Natural Resource Economics
- Calm Birth: Empowering Preparation for Childbirth
- October: The Second Decade, 1986-1996
- American Showcase: Artists' Representatives and Illustrators & Designers
- A Morel Hunter's Companion: A Guide to the True and False Morels
Epidemiologic Methods: Studying the Occurrence of Illness (Medicine)
Applied Longitudinal Analysis (Wiley Series in Probability and Statistics)
Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data
Fundamentals of Clinical Trials
Implementing an Electronic Health Record System (Health Informatics)
Handbook of Injury and Violence Prevention
