Product Description

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.

Product Details

• Amazon Sales Rank: #150712 in Books
• Published on: 2003-08-26
• Original language: English
• Dimensions: 1.39 pounds
• Binding: Hardcover
• 352 pages

Review
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 used…and the extensive discussion of confounding…this is a good, well-written piece of work.
Pharmaceutical Statistics, 2004

Book Info
Univ. of California, Berkeley. Provides techniques for analyzing risk factors and disease data with step-by-step extensions to include the use of binary regression. Features simple case studies illustrating elementary analyses to more complex regression modeling. Expanded-outline format.