Statistics for Epidemiology: 58 (Chapman & Hall/CRC Texts in Statistical Science) - Hardcover

Críticas

"Jewell's book can certainly be included in any group of useful books on statistics in epidemiology. It actually might be the one with which I would start." - Technometrics, February 2005, Vol. 47, No. 1 "This is a useful and thought-provoking book written by an expert in the field, providing a very valuable supplement to more introductory texts as well as a guide to more advanced sources." - Journal of the Royal Statistics Society "Good points of the book are the exercises, comments and further reading at the end of each chapter, the availability of the data sets usedand the extensive discussion of confoundingthis is a good, well-written piece of work." Pharmaceutical Statistics, 2004 'This book is excellent; a real breakthrough in texts on statistics in epidemiology, especially in its attention to causation and bias'. -Sander Greenland, Department of Epidemiology, UCLA "Using examples, this experienced statistician identifies scientific issues and clearly links them to statistical approaches. Statistical theory and formality are grounded in manageable yet realistic examples. Coverage includes the basics and important topics such as measurement error and causal analysis. The book has excellent references, an informative index and glossary." -ISI Short Book Reviews, August 2004

Reseña del editor

Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."

Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches.

Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies.