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eBook Methods in Observational Epidemiology (Monographs in Epidemiology and Biostatistics) download

by J. L. Kelsey,W. D. Thompson,A. S. Evans

eBook Methods in Observational Epidemiology (Monographs in Epidemiology and Biostatistics) download ISBN: 0195036573
Author: J. L. Kelsey,W. D. Thompson,A. S. Evans
Publisher: Oxford University Press (April 24, 1986)
Language: English
Pages: 384
ePub: 1353 kb
Fb2: 1561 kb
Rating: 4.7
Other formats: lit lrf txt azw
Category: Medics
Subcategory: Medicine

A comprehensive methods book covering a broad range of topics.

A comprehensive methods book covering a broad range of topics. I highly recommend this book both as a graduate level text, and as a basic reference for practicing professionals. The Yale Journal of Biology and Medicine.

Methods in Observational Epidemiology book. Goodreads helps you keep track of books you want to read. Start by marking Methods in Observational Epidemiology as Want to Read: Want to Read savin. ant to Read. by W. Douglas Thompson.

Methods in Observational Epidemiology. Evans and W. I think this book,Methods in Observational Epidemiology, might be very good reference to many scientists, especially biostatistist or epidemiologists. Recently Viewed and Featured. Select Format: Hardcover.

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Monographs in Epidemiology and Biostatistics. Methods in Observational Epidemiology. Although this book is intended for readers who have had introductory courses in epidemiology and biostatistics, even readers who do not fully comprehend the theory behind some of the techniques should understand the rationale for their use and be able to interpret results when they appear on a computer printout or in the literature.

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The various methods of monitoring and forecasting this disease receive comprehensive treatment. These methods include back-calculation, which the authors developed; interpretation of survey data on HIV prevalence; mathematical models for HIV transmission; and approaches that combine different types of epidemiological data. This means that epidemiologists (as well as all who engage in observational studies) must be on the lookout for problems that lurk in the design and execution of a study and must recognize the great potential for error. One significant class of errors is known as bias. Bias, as defined in epidemiology, is an error in design or execution of a study, which produces results that are consistently distorted in one direction because of nonrandom factors.

The rise of multivariate methods, clinical epidemiology, or nutritional epidemiology was reflected in the citation trends. Educational textbooks, practice-oriented books, books on epidemiological substantive knowledge, and on theory and health policies were much less cited. None of the 25 top-cited books had the theoretical or sociopolitical scope of works by Cochrane, McKeown, Rose, or Morris. Books were mainly cited to reference methods. Books first published in the 1980s continue to be most influential.

Kelsey LJ et a. e. Monographs in Epidemiology and Biostatistics. 2nd ed. New York: Oxford University Press; 1996. 7 Observational Studies and Bias in Epidemiology The Logic of Inference in Science Epidemiology is the science of studying health-related events that affect populations. Like all science, it is built on the fundamental belief that precise observation and measurement, combined with careful reasoning in the light of existing knowledge, is the most effective way to proceed with that study.

This is the first book to provide a complete picture of the design, conduct and analysis of observational studies, the most common type of epidemiologic study. Stressing sample size estimation, sampling, and measurement error, the authors cover the full scope of observational studies, describing cohort studies, case-control studies, cross-sectional studies, and epidemic investigation. The use of statistical procedures is described in easy-to-understand terms.
Comments: (7)
This was the textbooks for my epidemiology II course. While the book does go over most observational studies, I did not like the book because of the questions at the end of the chapters.

Example 1. Chapter 9, Case control, question 2. states: The table below presents results from a logistic regression analysis of breast cancer cases and unmatched controls from the same study as the ovarian cancer cases and controls described in this chapter... c. How much more likely to develop breast cancer is a woman whose menopause occurs at age 55 than...This is not a hard question, my issue was the "table below" did not have the information. Since it was the study from the chapter, I went in the chapter and found what I needed. Minor nuisance for me but several of my colleagues were stumped because they couldn't find the appropriate information on the table we were referred to.

Example 2, Chapter 12 (Sample size), it was difficult doing problem 2.d.. The question asks "2.) Assume that the annual incidence rate for breast cancer among those exposed to oral contraceptives is 4.4 per 10,000, and among those not exposed is 2.2 per 10,000. D. If 25% of women are exposed in the general population to oral contraceptives then how many cases and controls will be needed? When working this problem we need Pbar for the formula. The formula to get Pbar requires the proportion for P0 and P1. Well, in this case, since the exposure for P0 is 25%, that means exposure for P1 is different and what was given. I found out that I needed to do the proportion equation for P1, but that equation requires the odds ratio (OR). The OR is not given. Neither the chapter nor the question states what the OR should be. Our instructor told us that we should use 2, after we all got the problem wrong.

Example 3. Ch 13 measurement error. The incidence of a rare blood disorder, 8 out of 10,000 exposed individuals actually develop the disorder, as opposed to 3 out of 10,000 unexposed individuals. What would be the observed risk ratio if...sensitivity of 99% and a specificity of 98%. In the book, how to work the problem is not in an easy to read format. The problem is worked narrative style in the text, as opposed to by itself. Second the sample used exposed for both cases and controls. The question is provided the exposed and unexposed for cases. I worked the problem as what is in the book but I'm not sure if it is correct. The problem is when trying to calculate the new odds ratio. The box for unexposed cases are different from exposed controls.

The examples above are just three of the question issues I had with the book. I felt the book did not always do well explaining the proper terms to use for the formulas, or it did not show the examples in an easy to read and understand format. I also did not care for the index. I felt it was lacking. I remember looking for 'retrospective cohort' under R and it was not there, not even to say, go to Cohort, retrospective. Since, it is an entire Chapter, (CH 5), I would think they would have thought to put it under R. A minor nuisance but a nuisance nonetheless.

In all, this book was not for me.
A lot of material and content - good overview of the methods. A little on the "academia" side of providing information. I can see why instructors use it as a textbook in an epidemiology course.
This book could be half as long. The book makes straight forward topics seem complicated with their unnecessarily long explanations. It is also incredibly redundant.
Even though I purchased this for school, I find it an interesting read.
This was an recommended title for my class, and I am so glad I bought it. Love the way it is written. Great book to have for anyone interested or working in public health.
I drudged my way through the early chapters of this book. Most all-star topics in epidemiology are reliably laid out there. Unfortunately, the book is boring as hell. The one time I tried to use one of examples given in the book to calculate something (power or sample size?) I got hopelessly lost - the layout does not allow formulas to stand out. I found class notes and handouts much more useful than this book.
I think this book,Methods in Observational Epidemiology, might be very good reference to many scientists, especially biostatistist or epidemiologists.