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Provides an excellent introductory text for students on the principles and methods of statistical analysis in the life sciences, helping them choose and analyse statistical tests for their own problems and present their findings.

An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real-life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarites of field-based data and its interpretation, this book provides a superb introduction to statistical analysis helping students relate to their particular and often diverse data with confidence and ease.

To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included.

It will be extremely useful to undergraduates studying ecology, biology, and earth and environmental sciences and of interest to postgraduates who are not familiar with the application of multiavirate techniques and practising field biologists working in these areas.

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This thought-provoking book discusses the use of statistics in randomized clinical trials. Its aim is two-fold: firstly, it presents a clear account of the design and analysis of experiments in this setting which stresses the foundational issues involved. Secondly, the book seeks to develop the specific tools of analysis which can be derived from Neyman's model of restricted tests. The book is based on the author's many years of experience of clinical trials. Throughout, examples are used from a variety of types of study. As a result, all statisticians and research scientists who work on clinical trials will find this presentation clear and accessible, and very relevant to their own research interests.

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This illustrated textbook for biologists provides a refreshingly clear and authoritative introduction to the key ideas of sampling, experimental design, and statistical analysis. The author presents statistical concepts through common sense, non-mathematical explanations and diagrams. These are followed by the relevant formulae and illustrated by worked examples. The examples are drawn from all areas of biology, from biochemistry to ecology and from cell to animal biology. The book provides everything required in an introductory statistics course for biology undergraduates, and it is also useful for more specialized undergraduate courses in ecology, botany, and zoology.

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How Science Works: Evaluating Evidence in Biology and Medicine by Oxford University Press, USA




One week, red wine is good for the heart. The next week, new reports say it's bad for the health. So which is true? Anyone who's ever read science news with fascination, or who's ever been confounded by conflicting stories will appreciate this book. Taking a look at some true to life contemporary news stories, the author assesses recent studies on topics ranging from vitamin C and caffeine to pollution and cancer. With straight talk and a passion for the whole project of science, he demysifies the cult of the expert and sheds light on the nitty-gritty details of scientific processes. Any scientist loves a challenge, but the biggest challenge of all, observes Jenkins, is shared by scientists and nonscientitsts alike: how to make practical decisions in light of ambiguous evidence. Promising no simple answers, this book does offer excellent food for thought for people pondering that next glass of wine.Read more How Science Works: Evaluating Evidence in Biology and Medicine

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Just A Theory: Exploring The Nature Of Science

Buy Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) by Springer




Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.Read more Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

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Principles of Biostatistics (with CD-ROM)

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Statistical Monitoring of Clinical Trials: A Unified Approach (Statistics for Biology and Health) Description

The approach taken in this book is to studies monitored over time, what the Central Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem shows that test statistics involving very different types of clinical trial outcomes are asymptotically normal, this book shows that the joint distribution of the test statistics at different analysis times is asymptotically multivariate normal with the correlation structure of Brownian motion (“the B-value") irrespective of the test statistic. The so-called B-value approach to monitoring allows us to use, for different types of trials, the same boundaries and the same simple formula for computing conditional power. Although Brownian motion may sound complicated, the authors make the approach easy by starting with a simple example and building on it, one piece at a time, ultimately showing that Brownian motion works for many different types of clinical trials.

The book will be very valuable to statisticians involved in clinical trials. The main body of the chapters is accessible to anyone with knowledge of a standard mathematical statistics text. More mathematically advanced readers will find rigorous developments in appendices at the end of chapters. Reading the book will develop insight into not only monitoring, but power, survival analysis, safety, and other statistical issues germane to clinical trials.

Michael Proschan, Gordon Lan, and Janet Wittes are elected Fellows of the American Statistical Association. All have spent formative years in the Biostatistics Research Branch of the National Heart, Lung, and Blood Institute (NHLBI/NIH). While there, they were intimately involved in the design and statistical monitoring of large-scale randomized clinical trials, developing methodology to aid in their monitoring. For example, Lan developed, with DeMets, the now widely-used spending function approach to group sequential designs, whose properties were further investigated by Proschan. The B-value approach used in the book was introduced in a very influential paper by Lan and Wittes. The statistical theory behind conditional power was developed by Lan, along with Simon and Halperin, and was the cornerstone for the conditional error approach to adaptive clinical trials introduced by Proschan and Hunsberger. All three authors have expertise in adaptive methodology for clinical trials.

Michael Proschan is a Mathematical Statistician at the National Institutes of Health; Gordon Lan is Senior Director of Biometrics at Johnson & Johnson Pharmaceutical Research and Development, L.L.C.; Janet Wittes is President of Statistics Collaborative, a statistical consulting company she founded in 1990.

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Leading researchers and experimentalists update the field of flow cytometry with detailed accounts of their many new, improved, and well-tested protocols and applications. Among the cutting-edge methods presented here are techniques for the detection of terminal transferase ion leukemia, intracellular cytokines, intracellular/intranuclear antigens in leukemia and tumors, and cyclins. Protocols for performing functional studies include methods for assessing viability, transmembrane potential, oxidative metabolism, environmental particulate uptake, drug resistance, and cellular pharmacokinetics. The methods for examining DNA address cell cycle analysis, DNA/RNA analysis of solid malignancies, solid-tumor DNA ploidy analysis, apoptosis, and fluorescent in situ hybridization. Protocols for chromosome sorting and 5-6 color analysis are included as advanced methods. The techniques in this volume take advantage of the new instrumentation and probes that have recently been developed to advance the field of flow cytometry and improve standard methods.

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Cytokine Cellular Biology focuses on cell biology techniques for studying cytokines, cytokine receptors, and cytokine driven processes. Assays for human B cell responses, leucocyte migration, haematopoietic growth factors, macrophage activation by cytokines, RIA, IRMA, and ELISA assays, and quantitative biological assays for cytokines are all covered in detail. There are also updated chapters on studying cytokine regulation of endothelial cells; the measurement of proliferative, cytostatic, and cytolytic activity of cytokines; and the development of antibodies to cytokines. In addition there is a new chapter on the use of flow ctyometry and intracellular fluorescent staining. Written by experts in the field, Cytokine Molecular Biology and Cytokine Cellular Biology form a comprehensive and essential guide to cytokine research.

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Applied statisticians in many fields must 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, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. 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 methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.

Renal Cell Carcinoma: Translational Biology, Personalized Medicine, and Novel Therapeutic Targets Reviews

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This book provides a comprehensive look at renal cell carcinoma, exploring its biology as well as current and future molecular targets for renal cancer carcinoma.

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Main Features : Planning, Proposing, and Presenting Science Effectively: A Guide for Graduate Students and Researchers in the Behavioral Sciences and Biology

The book discusses planning research, writing a research proposal (whether for a formal proposal for thesis research, or for a research proposal for a funding agency such as the National Science Foundation), writing a research report (such as a graduate thesis, or a manuscript for publication in a research journal), and presenting research at research seminars and scientific meetings. The final chapter covers writing an effective CV. An appendix provides tips on how to write clearly. Throughout, the book is illuminated with personal examples from the authors' own experiences and there is a strong emphasis on problems associated with field studies. This concise guide is intended for students at all levels. The guidelines apply equally to independent projects for introductory biology, directed-study projects, undergraduate senior theses, master's theses, doctoral dissertations, and research aimed at publication.

Best Analysis of Multivariate Survival Data (Statistics for Biology and Health)

Analysis of Multivariate Survival Data (Statistics for Biology and Health) by Springer




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. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.Read more Analysis of Multivariate Survival Data (Statistics for Biology and Health)

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The Frailty Model (Statistics for Biology and Health)

Counting Processes and Survival Analysis (Wiley Series in Probability and Statistics)

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Perl Programming for Medicine and Biology (Series in Biomedical Informatics)

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Written for biomedical professionals and hospital practitioners interested in creating their own programs, Perl Programming for Medicine and Biology, discusses and reviews biomedical data resources, data standards, data organization, medicolegal and ethical conduct for data miners, and grants-related data sharing responsibilities. It teaches readers the basic Perl programming skills necessary for collecting, analyzing, and distributing biomedical data and provides solutions to in-depth problems that face researchers and healthcare professionals.

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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. Heping Zhang is Associate Professor of Biostatistics and Child Study at Yale University. In addition to the methodology and application of recursive partitioning, he is interested in developing statistical methods for analyzing correlated data, especially family and genetic studies, and brain imaging problems. Burton Singer, a member of the National Academy of Sciences, is Professor of Demography and Public Affairs at Princeton University. His research interests include combinatorial formulation of randomness, infectious disease epidemiology, and bio-demography of aging.

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Buy Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) by Springer




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.Read more Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

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Best Concepts and Challenges in Retinal Biology (Progress in Brain Research)

Concepts and Challenges in Retinal Biology (Progress in Brain Research) by Elsevier Science




In August 2000 a Festschrift was held at the Marine Biological Laboratory, Woods Hole, Massachusetts to celebrate the career of Professor John E. Dowling on the occasion of his 65th birthday. Containing contributions from more than 50 of John's colleagues, representing a Who's Who of the vision research community, this work not only provides a memento of the occasion, but will hopefully serve as a basic reference for future researchers in retinal biology.

The volume is divided somewhat arbitrarily into seven areas of retinal research containing chapters that present in some cases a broad overview of a particular topic, and in others an account of current research and studies in progress. These chapters exemplify the richness, diversity, and excitement of contemporary retinal research. They also remind us of how much more needs to be done before we understand fully the interrelationship between retinal neurons, the complex interactions between neurons and glial cells, and the mechanisms that govern retinal development.

A final chapter contributed by John Dowling provides an overview of past accomplishments, and offers some future perspectives on retinal research in the 21st century.Read more Concepts and Challenges in Retinal Biology (Progress in Brain Research)

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