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Recommended Books on Biostatistics
Likelihood, Bayesian and MCMC Methods in Quantitative Genetics Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a Bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments. Daniel Sorensen is a Research Professor in Statistical Genetics, at the Department of Animal Breeding and Genetics in the Danish Institute of Agricultural Sciences. Daniel Gianola is Professor in the Animal Sciences, Biostatistics and Medical Informatics, and Dairy Science Departments of the University of Wisconsin-Madison. Gianola and Sorensen pioneered the introduction of Bayesian and MCMC methods in animal breeding. The authors have published and lectured extensively in applications of statistics to quantitative genetics.
Medical Statistics at a Glance, Second Edition (At a Glance) Medical Statistics at a Glance provides a concise and accessible introduction and revision aid for undergraduate medical students and anyone wanting a straightforward introduction to this complex subject. Following the familiar, easy-to-use at a Glance format, each topic is presented as a double-page spread with key facts accompanied by clear, informative tables, formulae and graphs.
This new edition of Medical Statistics at a Glance:
- Contains a second colour throughout to enhance the visual appeal, making the subject even easier to understand
- Features worked examples on each topic, with emphasis on computer analysis of data rather than hand calculations
- Includes new topics on Rates and Poisson regression, Generalised linear models, Explanatory variables in statistical models and Regression models for clustered data.
- Has an accompanying website http://www.medstatsaag.com/containing supplementary material including multiple choice questions (MCQs) with annotated answers for self-assessment
Medical Statistics at a Glance will appeal to all medical students, junior doctors and researchers in biomedical and pharmaceutical disciplines.
Reviews of the last edition
"All medical professionals will come across statistics in their daily work and so a proper understanding of these concepts is invaluable. This is brought to you in this easily comprehensible succinct textbook.
.I unreservedly recommend this book to all medical students, especially those that dislike reading reams of text. This is one book that will not sit on your shelf collecting dust once you have graduated and will also function as a reference book."
4th Year Medical Student. Barts and the London Chronicle, Spring 2003,
vol.5, issue 1
Practical Statistics for Medical Research (Statistics Texts) Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research. Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.
Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health)
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).
Handbook of Statistical Analyses Using SAS, Second Edition Powerful software often comes, unfortunately, with an overwhelming amount of documentation. As a leading statistics software package, SAS is no exception. Its manuals comprise well over 10,000 pages and can intimidate, or at least bewilder, all but the most experienced users. A Handbook of Statistical Analyses using SAS, Second Edition comes to the rescue. Fully revised to reflect SAS Version 8.1, it gives a concise, straightforward description of how to conduct a range of statistical analyses. The authors have updated and expanded every chapter in this new edition, and have incorporated a significant amount of new material. The book now contains more graphical material, more and better data sets within each chapter, more exercises, and more statistical background for each method. Completely new topics include the following: · Data description and simple inference for categorical variables · Generalized linear models · Longitudinal data: Two new chapters discuss simple approaches, graphs, summary measure, and random effect models Researcher or student, new user or veteran, you will welcome this self-contained guide to the latest version of SAS. With its clear examples and numerous exercises, A Handbook of Statistical Analyses using SAS, Second Edition is not only a valuable reference, but also forms the basis for introductory courses on either SAS or applied statistics at any level, from undergraduate to professional.
Observational Studies An Observational study is an empiric investigation of the effects caused by a treatment, policy , or intervention in which it is not possible to assign subjects at random to treatment or control, as would be done in a controlled experiment. Observational studies are common in most fields that study the effects of treatments on people. The second edition of l Studies¿ is about 50 percent longer than the first edition, with many new examples and methods. There are new chapters on nonadditive models for treatment effects (Chapter 5) and planning observational studies (Chapter 11) and Chapter 9, on coherence, has been extensively rewritten. Paul R. Rosenbaum is Robert G. Putzel Professor, Department of Statistics, The Wharton School of the University of Pennsylvania. He is a fellow of the American Statistical Association.
Primer of Biostatistics (Primer of Biostatistics (Glantz)(Paperback)) Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author’s unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.
Analysis of Phylogenetics and Evolution with R (Use R)
The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.
An Introduction to Medical Statistics (Oxford Medical Publications) 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
Fundamentals of Biostatistics (with CD-ROM) FUNDAMENTALS OF BIOSTATISTICS (WITH CD-ROM) leads you through the methods, techniques, and computations necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.
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