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 biomed...

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased...

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex model...

This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of lik...

Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis tha...

CAUSAL INFERENCE IN STATISTICSA PrimerCausality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can...

A valuable overview of the most important ideas and results in statistical modelingWritten by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted mo...

* Includes a new chapter on logistic regression.* Discusses the design and analysis of random trials.* Explores the latest applications of sample size tables.* Contains a new section on binomial distribution.

Banish your fears of statistical analysis using this clearly written and highly successful textbook. Statistics for Veterinary and Animal Science Third Edition is an introductory text which assumes no previous knowledge of statistics. It starts with very basic methodology and builds on it to encompass some of the more advanced techniques that are currently used. This book will enable you to handle numerical data and critically appraise the veterinary and animal science literature....

Praise for the First Edition". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis."—Journal of the American Statistical AssociationFeatures newly developed topics and applications of the analysis of longitudinal dataApplied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state...

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately foc...

Praise for the Second Edition"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."—Statistics in Medicine"It is a total delight reading this book."—Pharmaceutical Research"If you do any analysis of categorical data, this is an essential desktop reference."—TechnometricsThe use of statistical methods for analyzing categorical data has increased ...