Modern Epidemiology Hardcover – Mar 14 2008
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Only drawback: no problems to work through or solutions to check your understanding.
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This highly regarded volume has no equal, as it has been not only an authoritative source of information but `the' reference on epi methods for almost a quarter century. For those looking for an introductory level textbook, Rothman's one by Oxford University Press is highly recommended, since the comprehensive `Modern Epidemiology' requires some previous exposure to the concepts and biostatistical methods presented.
The 3rd edition is an encyclopedic effort, brings methodological coherence to a whole new level, is highly readable, and confirms itself as the standard reference on epidemiological and clinical research for many more years to come. An outstanding scholarly achievement.
Definitely a must-have for anyone who needs to learn and apply basic/advanced epidemiological methods rigorously in clinical as well as general population settings.
1. It synthesizes contributions by Pearl and Rubin on the foundations of causal inference, and contributes its own perspective via the sufficient cause model. This is truly cutting edge, not to mention impeccably coherent.
2. The first third of the book is on study design, including measurement, sampling, and defining effects. This is just fantastic. Many methods textbooks jump right into approaches to analyzing data with little time taken to discuss how to make the data in the first place. This book provides a major corrective to that tendency.
3. In data analysis, a lot of attention is given to sparse data problems, which again is just great. So many textbooks overlook this problem, which is a huge omission.
4. The data analysis section includes discussion of up-and-coming data mining and non-parametric methods (e.g. BART, boosted regression, etc.) to characterize response surfaces in the service of causal inference. That's amazingly cutting edge for a textbook.
5. The meta-analysis section emphasizes simplicity and provides a very nice list of common errors that should be avoided.
6. The references are to state of the art literature not only in epidemiology, but also in econometrics, education research, and statistics. It's great to see such cross-fertilization across disciplines, and it shows how these various disciplines are converging, it seems, on common analytical tools for causal inference in observational studies.
There are lots of nice examples throughout the book too. For other social scientists out there, I highly recommend this as a primer on state of the art methods for carrying out observational studies.
His textbook lays down the philosophical and methodological foundation necessary to truly master the various aspects of modern epidemiology. Much like the mathematics student would not start their studies in calculus before learning algebra, so too must a reader have a familiarity with epidemiological concepts and methods before picking up this book. Nevertheless, for those willing to put in the effort, it is a highly logical and rewarding exposition on modern epidemiology.
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