Interpreting Epidemiologic Evidence: Strategies for Study Design & Analysis

By Gregory A. Wellenius, David A. Savitz, Gregory A. Wellenius

Book Overview

Evaluating the strength or persuasiveness of epidemiologic evidence is inherently challenging, both for those new to the field and for experienced researchers. There are a myriad of potential biases to consider, but little guidance about how to asses the likely impact on study results. This book offers a strategy for assessing epidemiologic research findings, explicitly describing the goals and products of epidemiologic research in order to better evaluate it successes and limitations. The focus throughout is on practical tools for making optimal use of available data to assess whether hypothesized biases are operative and to anticipate concerns at the point of study design in order to ensure that needed information is generated. Specific tools for assessing the presence and impact of selection bias in both cohort and case-control studies, bias from non-response, confounding, exposure measurement error, disease measurement error, and random error are identified and evaluated. The potential value of each approach as well as its limitations are discussed, using examples from the published literature. Such information should help those who generate and interpret epidemiologic research to apply methodological principles more effectively to substantive issues, leading to a more accurate appraisal of the current evidence and greater clarity about research needs.

• Format: Hardcover
• Language: English
• ISBN: 019510840X
• ISBN13: 9780195108408
• Release Date: June 2001
• Publisher: Oxford University Press
• Length: 336 Pages
• Weight: 1.38 lbs.
• Dimensions: 1.1" x 6.6" x 9.5"

Customer Reviews

Required reading for every epidemiologist

This book by David Savitz is an important and welcome addition to the epidemiologist's library. I started reading it after my first year of doctoral studies but I did not fully appreciate the usefulness of Dr. Savitz's insights until after I started doing independent research as a academic physician-epidemiologist. The book is well-written and presents the various methodologic challenges faced by epidemiologic researchers in a logical and coherent way. The chapter on selection bias is particularly illuminating. Remarkably, all the information is presented in lucid prose without the use of mathematical notation. If you would like to get the most out of your research endeavours, this book is for you. I think it should be required reading for every epidemiologist.