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This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded to include the software package R. David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning. Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emory's Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.

Biomathematics. Biometry. Biostatistics --- Epidemiology --- medische statistiek --- biostatistiek --- epidemiologie --- biometrie

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Analysis of Genetic Association Studies is both a graduate level textbook in statistical genetics and genetic epidemiology, and a reference book for the analysis of genetic association studies. Students, researchers, and professionals will find the topics introduced in Analysis of Genetic Association Studies particularly relevant.  The book is applicable to the study of statistics, biostatistics, genetics and genetic epidemiology.    In addition to providing derivations, the book uses real examples and simulations to illustrate step-by-step applications.  Introductory chapters on probability and genetic epidemiology terminology provide the reader with necessary background knowledge.  The organization of this work allows for both casual reference and close study. 

Statistical science --- Biomathematics. Biometry. Biostatistics --- medische statistiek --- biostatistiek --- statistiek --- biometrie

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One of the major challenges for the scientific community, a challenge that has been seen in many business disciplines, is the exponential increase in data being generated by new experimental techniques and research. A single microarray experiment, for example, can generate thousands of data points that need to be analyzed, and this problem is predicted to increase. As new techniques in areas such as genomics and proteomics continue to be adopted into the mainstream as the costs fall, the need for effective mechanisms for synthesizing these disparate forms of data together for analysis is of paramount importance. But the sheer volume of data means that traditional techniques need to be augmented by approaches that elicit knowledge from the data, using automated procedures. Data mining provides a set of such techniques, new techniques to integrate, synthesize, and analyze the data, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.

Biomathematics. Biometry. Biostatistics --- bio-informatica --- data mining --- biometrie

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Computational neurosciences and systems biology are among the main domains of life science research where mathematical modeling made a difference. This book introduces the many different types of computational studies one can develop to study neuronal systems. It is aimed at undergraduate students starting their research in computational neurobiology or more senior researchers who would like, or need, to move towards computational approaches. Based on their specific project, the readers would then move to one of the more specialized excellent textbooks available in the field.   The first part of the book deals with molecular systems biology. Functional genomics is introduced through examples of transcriptomics and proteomics studies of neurobiological interest. Quantitative modelling of biochemical systems is presented in homogeneous compartments and using spatial descriptions. A second part deals with the various approaches to model single neuron physiology, and naturally moves to neuronal networks. A division is focused on the development of neurons and neuronal systems and the book closes on a series of methodological chapters. From the molecules to the organ, thinking at the level of systems is transforming biology and its impact on society. This book will help the reader to hop on the train directly in the tank engine.

Biomathematics. Biometry. Biostatistics --- Neuropathology --- neurologie --- bio-informatica --- biometrie

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Evidence-based medicine aims to apply the best available evidence gained from the scientific method to medical decision making. It is a practice that uses statistical analysis of scientific methods and outcomes to drive further experimentation and diagnosis. The profusion of evidence-based medicine in medical practice and clinical research has produced a need for life scientists and clinical researchers to assimilate biostatistics into their work to meet efficacy and practical standards. Practical Biostatistics provides researchers, medical professionals, and students with a friendly, practical guide to biostatistics. With a detailed outline of implementation steps complemented by a review of important topics, this book can be used as a quick reference or a hands-on guide to effectively incorporate biostatistics in clinical trials.

Statistical science --- Mathematics --- Biomathematics. Biometry. Biostatistics --- Human medicine