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).
Customer Reviews:
Most Elegant Account of Bioinformatics.......2004-11-27
I was impressed with the 1st edition of this book for its most comprehensive and elegant of statistical techniques in bioinformatics. The book is slightly below the level of the now classic M S Waterman (1995)book:Introduction to Computational Biology: Maps, Sequences and Genomes. But this book is more update in some areas and has much more background materials on probability and statistics, which should provide a solid basis for understanding bioinformatics. Its pedagorical sense is unparalleled. It would make a very good choice for a stat/math oriented introduction to bioinformatics (as opposed to algorithimc/database oriented approach in cs).
Average customer rating:
- I was forced to buy it.
- Start Here for Medical Statistics
- another fine statistics text for medical students
|
An Introduction to Medical Statistics (Oxford Medical Publications)
Martin Bland
Manufacturer: Oxford University Press, USA
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Practical Statistics for Medical Research (Statistics Texts)
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Statistical Questions in Evidence-Based Medicine
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Medical Statistics Made Easy
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Statistics in Medicine, Second Edition
ASIN: 0192632698 |
Book Description
Now in its Third Edition, An Introduction to Medical Statistics continues to be an invaluable textbook for medical students, doctors, medical researchers, nurses, members of professions allied to medicine as well as all those concerned with medical data. The material covered includes all the statistical work that would be required for a course in medicine and for the examinations of most of the Royal Colleges. It includes the design of clinical trials and epidemiological studies, data collection, summarizing and presenting data, probability, standard error, confidence intervals and significance tests, techniques of data analysis including multifactorial methods and the choice of statistical method, problems of medical measurement and diagnosis, vital statistics, and calculation of sample size. The new edition describes the design and analysis of medical research studies in a clear and user friendly manner. The Third Edition includes new topics such as consent in clinical trials, design and analysis of cluster-randomized trials, ecological studies, conditional probability, repeated testing, random effects models, intraclass correlation, and conditional odds ratios. Material which is encountered only at the postgraduate level has been indicated clearly in the text to facilitate ease of use. The book is firmly grounded in medical data, particularly in medical research, and includes real illustrative examples. There are 100 multiple choice questions and 17 long questions involving calculations to which fully explained solutions are provided. A new companion volume, Statistical questions in evidence-based medicine (Bland and Peacock, 2000) refers directly to this new edition. This new book of questions and answers includes no calculations and is complementary to the exercises given here. Reviewers comments 'If you want to understand some of the statistical ideas important to medicine but fear being overwhelmed by mathematics you will welcome An Introduction to Medical Statistics.' British Medical Journal 'At last I have a book on medical statistics that I can safely recommend to my students!...One of the pleasures of the book is that it contains real data...' Journal of the Royal Statistical Society
Customer Reviews:
I was forced to buy it........2005-10-26
It's an okay text but the author quotes himself a lot. I just wish all the tables and formulas were in a specific location and the symbols for the formulas defined each time a new one was presented.
Start Here for Medical Statistics.......2002-05-21
Clearly and logically set out starting with the reasons for medical statistics and how studies are designed and moving logically to the methods used. All practicing doctors need to know something about medical statistics. This is a very good place to start. It is written for non-mathematicians but has enough mathematics to justify the information presented and stimulate further interest. It is a relatively short book with clear logical explanations rather than flashy illustrations and computer graphics. Also provides exercises, essential in all mathematical subjects.
another fine statistics text for medical students.......2000-06-14
This is the second edition of an excellent introductory text for medical students similar to Doug Altman's. Bland covers the basics: designing clinical trials and epidemiological studies, data collection, summarizing data, basic probability, standard errors and confidence intervals along with tests of significance (both parametric and nonparametric). Various types of regression methods are covered and this edition adds Cox proportional hazards models and meta-analysis. Many exercises and examples are taken from Bland's 20 years of experience as a medical statistician. It is noteworthy for dealing with sample size issues in Chapter 18. Mortality statistics and life tables are covered in Chapter 16.
The book is not quite as advanced as Altman's.
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.
Customer Reviews:
Clear and Consice but too Compact.......2004-11-20
While what the book does explain about the statistical theory mentioned, it is too compact for what it tries to explain. There are also no answers to the excercises, which would be quite helpful given some of the questions asked. It's great for applications and is a good handbook, but for a thorough explanation of everything involved, I recomend getting a bigger textbook! For my 4th year Generalized Linear Models stats class, this book is helpful, but at times too compact to be more useful.
the most clearly written book on the topic.......2002-08-16
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.......2002-08-04
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.......2002-06-18
...
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.
-.......2000-04-04
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_.
Book Description
An essential introductory text linking traditional biostatistics with bayesian methods
In recent years, Bayesian methods have seen an explosion of interest, with applications in fields including biochemistry, ecology, medicine, oncology, pharmacology, and public health. As an interpretive system integrating data with observation, the Bayesian approach provides a nuanced yet mathematically rigorous means of conceptualizing biomedical statistics--from diagnostic tests to DNA evidence.
Biostatistics: A Bayesian Introduction offers a pioneering approach by presenting the foundations of biostatistics through the Bayesian lens. Using easily understood, classic Dutch Book thought experiments to derive subjective probability from a simple principle of rationality, the book connects statistical science with scientific reasoning. The author shows how to compute, interpret, and report Bayesian statistical analyses in practice, and illustrates how to reinterpret traditional statistical reporting--such as confidence intervals, margins of error, and one-sided p-values--in Bayesian terms. Topics covered include:
* Probability and subjective probability
* Distributions and descriptive statistics
* Continuous probability distributions
* Comparing rates and means
* Linear models and statistical adjustment
* Logistic regression and adjusted odds ratios
* Survival analysis
* Hierarchical models and meta-analysis
* Decision theory and sample size determination
The book includes extensive problem sets and references in each chapter, as well as complete instructions on computer analysis with the versatile SAS and WinBUGS software packages as well as the Excel spreadsheet program. For professionals and students, Biostatistics: A Bayesian Introduction offers an unique, real-world entry point into a remarkable alternative method of interpreting statistical data.
Book Description
This is an expanded edition of the author's "Multivariate Statistical Analysis." Twice as long, it includes all the material in that edition, but has a more extensive treatment of introductory methods, especially hypothesis testing, parameter estimation, and experimental design. It also introduces time series analysis, decision analysis, and more advanced probability topics (see the accompanying table of contents). It has been used as a textbook at the graduate level at over 300 leading universities, in over a dozen academic disciplines, including education, business, and the social and health sciences. Like its abridged edition, it has been acclaimed for its lucid treatment of difficult statistical concepts.
Customer Reviews:
A truly great book!!.......2006-07-12
I read this book even after 15 years since my Ph.D. data analysis course and thank my prof. everytime. It lays out the conceptual foundations of different analyses in a really neat way.
If only it would also come with the instructions for software such as SPSS!
Yo Baby, Sammy is Da' Hoochie Daddy.......2001-11-27
I just wish he would write a new book and focus it around SPSS. Although the book was published long ago, it is written to those struggling in their Multivariate classes. It is very clear and concise. I could have read this in high school and understood every word. The book is not focused on linear algebra or higher level mathematics. Instead, he provides a clear conceptual understanding of the major techniques. If you read this much, buy the book and read it. I guarentee that in no time you will be asking your prof questions that will boggle his or her mind and you will score you some brownie points. Further, after reading it you will love the subject and study stats naked. Even if you decide not to keep this as a desk reference, you can always sell it to another student and use the cash to buy some beer. After reading this review, I only ask that you write Dr. Sam and tell him to keep writing. Tell him that we will revoke his Visa if he doesn't. I love you SAM. Peace to the world. Long live Dallas, TX! This book helped me earn an A+ in advanced stats in the MBA that I am enrolled in.
Statistical Analysis by Kachigan.......2001-02-06
This book has an excellent treatment of the various statistical methodologies. It has an easy-to-read treatment of basic probability with a gradual introduction into conditional prob. The coverage of mean, variance, weighted mean and the standard deviation is good. I was impressed with the explanation of the z statistic and hypothesis testing. The author had a good rendition of regression and correlation in single, as well as, multiple models. The style of this work is a belles lettres
treatment-almost akin to reading a story. Despite the simplicity, there is enough complexity for the quantitatively oriented readers. I would like to see an update of this book with some cross-referencing to the internet and more modern terminology. Despite the fact that it is an older work, there are few books which explain the material with the clarity of this text. Its weaknesses may be overcome by utilizing the REA Statistics or the Schaum's outline. This book is excellent for students who find math hard to comprehend. It serves all levels of students from the most advanced to the students requiring more time with the material.
Afraid of Statistics? This book is for you!.......1997-09-27
This volume is an excellent introduction for someone who needs a conceptual understanding of statistics without the rigor or depth of a specialist. The presention is very non-technical with each new subject carefully developed. Ideal for the general business or social science undergraduate or anyone needing a brief and relatively painless encounter with a vital subject.
Book Description
Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence. Assuming only a modest mathematical background, the book uses real-life examples from the forensic science literature and forensic case-work to illustrate relevant statistical concepts and methods.
Opening with a brief overview of the history and use of statistics within forensic science, the text then goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis on the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence are discussed throughout with a particular focus on DNA, fibres and glass.
An invaluable introduction to the statistical interpretation of forensic evidence; this book will be invaluable for all undergraduates taking courses in forensic science.
- Introduction to the key statistical techniques used in the evaluation of forensic evidence
- Includes end of chapter exercises to enhance student understanding
- Numerous examples taken from forensic science to put the subject into context
Download Description
Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence. Assuming only a modest mathematical background, the book uses real-life examples from the forensic science literature and forensic case-work to illustrate relevant statistical concepts and methods. Opening with a brief overview of the history and use of statistics within forensic science, the text then goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis on the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence are discussed throughout with a particular focus on DNA, fibres and glass. An invaluable introduction to the statistical interpretation of forensic evidence; this book will be invaluable for all undergraduates taking courses in forensic science. Introduction to the key statistical techniques used in the evaluation of forensic evidence Includes end of chapter exercises to enhance student understanding Numerous examples taken from forensic science to put the subject into context
Customer Reviews:
An excellent companion.......2007-02-06
It is a very useful an helpful book... everyone involved in teaching may find it an excellent guide to help and involve students of forensics into the effective use of statistics.
Average customer rating:
- Warning: Text contains many errors
|
Health and Numbers: A Problems-Based Introduction to Biostatistics
Chap T. Le
Manufacturer: Wiley-Liss
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ASIN: 0471416614 |
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.
Customer Reviews:
Warning: Text contains many errors.......2007-02-17
+ Note: This review was originally written for another book from this author, but touches on my experience with this text as well.
It is mind-boggling how one can have so many mistakes in a textbook like this. This book was required for our Biostatistics II class, and I am really displeased by all of the mistakes. What is the worst offense is that some of the answers for test questions are wrong. For example, the one way ANOVA problem 7.21 has the wrong F-statistic: the wrong answer. Others in the class have complained about this text as well. This is simply amazing to me, and leaves me not trusting much of the text in this book.
His other book "Health and Numbers" is just as terrible; the professor that used that book for class made a list of the mistakes they found, and us students in that class found many more along the way. I ran into another statistics book that takes all of the procedures step-by-step and really made it simple for me. This book, and his other are not recommended.
Book Description
This guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.
Average customer rating:
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Introduction to Biostatistics
Thomas Glover , and
Kevin Mitchell
Manufacturer: McGraw-Hill Science/Engineering/Math
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Introductory Biological Statistics
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ASIN: 0072418419 |
Book Description
Introduction to Biostatistics is an accessible and complete introduction for students to the use of statistics in the biological sciences. Designed for sophomore- and junior-level biology, environmental science, biochemistry, and health science students who are taking an introductory statistics course, there are examples throughout the text taken from many areas in the life sciences, including genetics, physiology, ecology, agriculture, and medicine.This new text emphasizes the relationships among probability, probability distributions, and hypothesis testing. The authors have highlighted the expected value of various test statistics under the null and research hypotheses as a way to understand the methodology of hypothesis testing. In addition, they have incorporated nonparametric alternatives to many situations along with the standard parametric analysis.
Customer Reviews:
Excellent explanations.......2007-03-08
My biostats professor is one of the worst teachers I've ever had, so I basically have to teach myself the material. This book definitely explains the concepts wonderfully and provides a lot of examples to explain them even further. I don't mind studying out of it at all, and I normally hate math textbooks.
Book Description
A companion website is available for this text! Click here.
Statistics for Health Care Professionals is an accessible guide to understanding statistics within health care practice. Focusing on quantitative approaches to investigating problems, the book introduces the basic rules and principles of statistics.
Challenging the notion that statistics are often incomprehensible and complex to use, the authors begin by presenting a `how to' section explaining how specific statistical tests can be performed. They also help readers to understand the language of statistics, which is often a stumbling block for those coming to the subject for the first time. The reader is taught how to calculate statistics by hand as well as being introduced to computer packages to make life easier, and then how to analyse these results.
As the results of health care research are so integral to decision-making and developing new practice within the profession, the book encourages the reader to think critically about data analysis and research design, and how these can impact upon evidence based practice. This critical stance is also crucial in the assessment of the many reports and documents issued within the health industry.
Statistics for Health Care Professionals includes practical examples of statistical techniques throughout, and the exercises within and at the end of each chapter help readers to learn and to develop proficiency. There is also a glossary at the end of the book for quick and easy referencing.
This book is essential reading for those coming to statistics for the first time within a health care setting.
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