Design and Analysis of Experiments in the Health Sciences: van Belle/Design and Analysis Health

**An accessible and practical approach to the design and analysis of experiments in the health sciences** __Design and Analysis of Experiments in the Health Sciences__ 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. 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: * Completely randomized designs * Randomized block designs * Factorial designs * Multilevel experiments * Repeated measures designs 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. Requiring only a basic background in statistics, __Design and Analysis of Experiments in the Health Sciences__ 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. Content: Chapter 1 The Basics (pages 1–30): Chapter 2 Completely Randomized Designs (pages 31–61): Chapter 3 Randomized Block Designs (pages 63–92): Chapter 4 Factorial Designs (pages 93–115): Chapter 5 Multilevel Designs (pages 117–133): Chapter 6 Repeated Measures Designs (pages 135–148): Chapter 7 Randomized Clinical Trials (pages 149–178): Chapter 8 Microarrays (pages 179–205): 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-- Gerald Van Belle, Kathleen F. Kerr. Includes Bibliographical References (p. 207-216) And Indexes. "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"-- Provided by publisher * The principles and rules presented in this book apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies. * Current laboratory work of fundamental interest, i.e.