Design and Analysis Health - by Kathleen F Kerr & Gerald Van Belle (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br>"This volume provides technical professionals and students with three uniquely integrative enhancements to the study of predictive modeling not typically found in data-mining books: an applied approach, immediate practice using Microsoft Excel, and easy-to-use access to multiple online model-building tools. Since actual datasets are employed, users deal with real-life modeling issues and situations such as handling missing values, applying variable transformations, and addressing outliers, among others. An easy-to-learn Microsoft Excel add-in (Predictive MinerXL) and all applicable datasets are available for free on an associated Web site"--<p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>An accessible and practical approach to the design and analysis of experiments in the health sciences</b></p> <p><i>Design and Analysis of Experiments in the Health Sciences</i> provides a balanced presentation of design and analysis issues relating to data in the health sciences and emphasizes new research areas, the crucial topic of clinical trials, and state-of-the- art applications.</p> <p>Advancing the idea that design drives analysis and analysis reveals the design, the book clearly explains how to apply design and analysis principles in animal, human, and laboratory experiments while illustrating topics with applications and examples from randomized clinical trials and the modern topic of microarrays. The authors outline the following five types of designs that form the basis of most experimental structures: </p> <ul> <li>Completely randomized designs</li> <li>Randomized block designs</li> <li>Factorial designs</li> <li>Multilevel experiments</li> <li>Repeated measures designs</li> </ul> <p>A related website features a wealth of data sets that are used throughout the book, allowing readers to work hands-on with the material. In addition, an extensive bibliography outlines additional resources for further study of the presented topics.</p> <p>Requiring only a basic background in statistics, <i>Design and Analysis of Experiments in the Health Sciences</i> is an excellent book for introductory courses on experimental design and analysis at the graduate level. The book also serves as a valuable resource for researchers in medicine, dentistry, nursing, epidemiology, statistical genetics, and public health.</p><p/><br></br><p><b> Review Quotes </b></p></br></br><br><p>"Overall, Design and Analysis of Experiments in the Health Sciencesis a balanced and approachable text suitable for a graduate level experimental design course, and will prove particularly useful to practitioners in the health sciences." (<i>Journal of Biopharmaceutical Statistics</i><i>, </i> 1 January 2013)</p> <p>"The book will be a valuable resource for researchers in medicine, dentistry, and the public health sciences. The authors are faculty members in the Department of Biostatistics at the University of Washington in Seattle." (<i>Journal of Clinical Research Best Practices</i>, 1 September 2012)</p> <p> </p><br><p/><br></br><p><b> About the Author </b></p></br></br><p><b>GERALD VAN BELLE, PhD, </b> is Professor Emeritus in the Departments of Biostatistics and Environmental and Occupational Health Sciences at the University of Washington. A Fellow of the American Statistical Association and the American Association for the Advancement of Science, he has published more than 140 articles in the areas of experimental design and data characterization as well as analysis with application to neurodegenerative diseases, effects of air pollution on health and toxicology, and clinical trials in resuscitation outcomes research.</p> <p><b>KATHLEEN F. KERR, PhD, </b> is Associate Professor of Biostatistics at the University of Washington. A former Burroughs Wellcome postdoctoral fellow in mathematics and molecular biology, Dr. Kerr currently serves as associate editor of <i>PLoS Genetics and Statistical Applications in Genetics and Molecular Biology.</i> Her research interests include gene expression microarrays, statistical genetics, experimental design, and biomarker research.</p>
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