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This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.

Medical statistics --- Biometry --- Data Interpretation, Statistical --- Models, Statistical --- Data processing --- Medical statistics. --- Biometry. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Health --- Health statistics --- Medicine --- Statistical methods --- Data processing. --- Medical statistics - Data processing --- Biometry - Data processing

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This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields.The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including ‘portable power’ formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice.Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software.Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable.

Statistical science --- Biomathematics. Biometry. Biostatistics --- bio-informatica --- statistiek --- statistisch onderzoek --- Experimental design. --- Disseny d'experiments

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Biomathematics. Biometry. Biostatistics --- Mathematical statistics --- Biometry --- Biométrie --- 519.2 --- 57.087.1 --- 519.2:57 --- binomiale verdeling --- biologie --- biometrie --- biostatistiek --- chi-kwadraattoetsen --- correlaties --- hypothesen testen --- lineaire regressie --- multipele regressie --- oefeningen --- oplossingen --- regressie-analyse --- variantieanalyse --- wiskunde --- Probability. Mathematical statistics --- Biometry. Statistical study and treatment of biological data --- Waarschijnlijkheidsrekening. Mathematische of wiskundige statistiek --- statistiek en biologie --- 57.087.1 Biometry. Statistical study and treatment of biological data --- 519.2 Probability. Mathematical statistics --- Biométrie

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This greatly expanded second 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 second edition continues to use the unique "lecture-book" format of the first (1996) edition with the addition of three new chapters on advanced topics: Chapter 7: Parametric Models Chapter 8: Recurrent events Chapter 9: Competing Risks. Also, the Computer Appendix has been revised to provide step-by-step instructions for using the computer packages STATA (Version 7.0), SAS (Version 8.2), and SPSS (version 11.5) to carry out the procedures presented in the main text. The original six chapters have been modified slightly to expand and clarify aspects of survival analysis in response to suggestions by students, colleagues and reviewers, and to add theoretical background, particularly regarding the formulation of the (partial) likelihood functions for proportional hazards, stratified, and extended Cox regression models 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.

Mathematical statistics --- Biomathematics. Biometry. Biostatistics --- Survival analysis (Biometry) --- Survival Analysis --- Programmed Instruction --- Distribution (Probability theory. --- Statistics. --- Epidemiology. --- Mathematical statistics. --- Probability Theory and Stochastic Processes. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Diseases --- Public health --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- 610.72 --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Failure time data analysis --- Survival Analysis. --- Programmed Instructions as Topic. --- Probabilities. --- Statistics . --- Probability --- Combinations --- Chance --- Least squares --- Risk

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Medicine deals with treatments that work often but not always, so treatment success must be based on probability. Statistical methods lift medical research from the anecdotal to measured levels of probability. This book presents the common statistical methods used in 90% of medical research, along with the underlying basics, in two parts: a textbook section for use by students in health care training programs, e.g., medical schools or residency training, and a reference section for use by practicing clinicians in reading medical literature and performing their own research. The book does no

Biometry --- Models, Statistical. --- Medical statistics. --- Statistique médicale --- methods. --- Medical statistics --- Statistics as Topic --- Models, Statistical --- methods --- Statistique médicale --- ELSEVIER-B EPUB-LIV-FT --- Health --- Health statistics --- Medicine --- Statistics --- Statistical methods --- Chromatographic analysis --- Analysis, Chromatographic --- Chromatography --- Analytical chemistry --- Phase partition --- Chromatographic analysis. --- Biometry - methods