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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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Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.

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Biomedical research involving animals remains essential for the advancement of the medical, veterinary, agricultural, and biological sciences. Following in the footsteps of its predecessors, the Handbook of Laboratory Animal Science, Volume II, Third Edition: Animal Models explains in great detail the comparative considerations underlying the choice of animal species and strains in different research disciplines.

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Macromolecules are the proteins and nucleic acids upon which life depends. Understanding the action of biological macromolecules (giant molecules) requires detailed knowledge of their structures. Most of the more than ten thousand known structures of protein and nucleic acids were obtained by x-ray crystallography, the standard mechanism for determining protein structure. Essentially, proteins are frozen into rigid crystals, which can be stacked up in a repeating pattern--like supermarket displays. The structure of each individual crystal can be determined by the way x-rays are bent when they pass through the composite crystal. Protein structure is essential when investigating protein interactions and planning drug development.
Crystallography Made Crystal Clear, Second Edition explains how scientists discover the structures of the macromolecules. Scientists do not see these molecules directly. Instead, they build models as a means of interpreting data from x-ray diffraction by crystals, or by irradiation by other forms of energy. Users of these models need to know how they are obtained in order to know what they are seeing when they study a model of a macromolecule. They also need to know how to judge whether conclusions they draw from the molecular models are really supported by the models. This book uses visual and geometric models to help readers understand the mathematics that forms the basis of x-ray crystallography.
The field of protein crystallography is growing every day and has been instrumental in discovering the molecular principles of biology and in discovering new drugs, such as the recent protease inhibitors for AIDS. The field includes the largest percentage of Nobel prizes than any other scientific discipline. Every major university and drug company has a protein crystallography laboratory and this book is an invaluable aid to those wishing to practice protein crystallography
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Key Features
* Provides clear, understandable descriptions of principles of X-ray crystallography
* Leads reader through unintimidating and thorough explanations of the underlying mathematics
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* Includes brief descriptions of other diffraction methods (neutron, electron, Laue) and the kinds
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* Introduces other methods of macromolecular structure determination (NMR spectroscopy and
* homology modeling), and provides guidance in judging the quality of these models

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Matrix Population Models, Second Edition includes expanded treatment of stochastic and density-dependent models, sensitivity analysis, and statistical inference, and new chapters on parameter estimation, structured population models, demographic sto-chasticity, and applications of matrix models in conservation biology. This book is an indispensable reference for graduate students and researchers in ecology, population biology, conservation biology, and human demography.

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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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With contributions from an international team of leading researchers, the book pulls together updated research results in the area of HIV/AIDS modeling to provide readers with the latest information in the field. Topics covered include: AIDS epidemic models; vaccine models; models for HIV/cell dynamics and interactions; cellular kinetics; viral dynamics with antiviral treatments; modeling of drug resistance and quasispecies. Extensive deterministic models, statistical models, stochastic models and state space models on treating AIDS patients with anti-retroviral drugs are provided, as well as an in-depth discussion of these models. The book also contains updated reviews on mathematical models for assessing effects of AIDS vaccines, statistical methods for analyzing clinical trial data on AIDS vaccines, and overviews of models and statistical methods for assessing drug resistance of HIV to anti- retroviral drugs. Some important statistical methods specific to the intervention and prevention of HIV epidemic are also discussed. This will be a useful reference source for graduate students and researchers in biomathematics and biostatistics, as well as for HIV/AIDS epidemiologists and clinical investigators learning quantitative methods to study AIDS epidemics and HIV infection.

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