Customer Reviews

Studying a Study and Testing a Test: How to Read the Medical Evidence (Core Handbook Series in Pediatrics)

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I bought this book 18 months ago and never cracked it til last week. I have worked as a Medicare medical director for five years, and someone should have given each of us this book the day we started. It is a very good survey of how to assess clinical trials. You will not finish this book and then plan trials for "big pharma" nor will you have a PhD in statistics (one recent reviewer of this book at Amazon was appalled by what he found to be several semantic errors or math errors - let's bear in mind the book has thrived through 5 editions and the author has a PhD from Johns Hopkins.) But if you are thinking about what makes clinical trials "tick" and you want a thorough (300 pp) but not numbing viewpoint, this is an excellent book. I also found the book very well written, I was able to go through 50 pages at a pop and felt I was really absorbing it. Embarassingly, considering jobs I have held, I would have been hesitant to give a concise explanation of a "case control" versus "cohort" study and Riegelman makes this sort of distinction fundamental and clear early one without belaboring how important it is (you can tell how important it is). The author, Riegelman, is a senior professor at the Geo Washington Univ School of Medicine. The book is intended for medical students (smart insightful ones), MPH students, those reviewing/evaluating the clinical literature (the evidence-based-medicine world). He explains things in a way that makes you think you'll remember it for years. For a different style and result(imagine you are actually designing a clinical trial in your niche of medicine from Step A to Step Z) see the Brian Hayes/David Sackett books (several versions) on "clinical epidemiology" or "evidence based medicine."

A wonderful book to help out with all those journal clubs and intro epi courses we have to take. I used it in medical school and continue to use it during residency. I refer to it before journal clubs and before discussing studies with medical students. Easy to read, concise, great overview of the subject! Highly reccomended!

Great book that was really helpful as I was reviewing the literature and preparing to write a grant proposal.

Very nicely written... for non-math oriented people. Very "user" friendly. Provides examples and explanations clearly.

This is a textbook, but it is not difficult to read and is easy to understand. I would recommend it to anyone who wants to be able to read and evaluate any kind of scientific study, and medical studies in particular. The online component of this edition no longer works, but a quick google search will find you the latest version.

I started to review the book, but could not stand it after reading a few pages. On p 56 interaction is incorrectly defined and is referred as being subject to low power. This is neither correct nor helpful. While tests of interaction may have less power then main effect tests, they can still be performed and are often critical to an analysis.
P 323 Estimation is incorrectly limited to effect size. Inference is incorrectly equated with hypothesis testing. Inference is both hypothesis testing and estimation. The latter includes both point and interval estimates. Later on the page the book states data in graphic form are hard to communicate. This is flat wrong. "One picture is worth one thousand words" and at least as many statistics. Students and researchers today are routinely urged to plot their data before undertaking an analysis.
P 324 Asserts that mean is a special case of average. The author incorrectly asserts that averages are weighted and means use weights of one. This is news to most statisticians who routinely use weighted means. In EXCEL the average function is unweighted.
P 329 uses the neologisms univariable and bivariable rather than the English words univariate and bivariate. The jargon multivariable is also used, but should not be encouraged. If the analyst means multiple regression or several variables, then that should be said.
P 329 asserts the choice of statistical technique depends on scale of measurement. This is far too narrow, the choice depends on the research design. The author gives an incomplete definition of continuous and numerical discrete scales. A critical point is that for either the distance from, for example, 1 to 3 is twice the distance from 1 to 2.
P 331 Ordinal is required to have at least three values and "... at most a limited number of values, such as stages of cancer." This is flat wrong. A binary variable can be treated as ordinal and an ordinal variable can have an unlimited number of values. For example non parametric statistics can be based on ranks with one rank for each value of the original discrete data. Moreover, not all cancer stages are ordinal, for example TNM staging is not ordinal. Colon cancer stages 3A and 3B are not ordered.

At this point I decided not to read on as I had had enough of a cardiovascular work out. This book should be avoided.
Studying a Study and Testing a Test: How to Read the Health Science Literature

This book assumes that the reading of medical texts is not just something that is self-evident, but something that can be learned by reading the book. "The traditional course in reading the medical literature consists of "Here's the New England Journal of Medicine. Read it!". Unlike this "sink or swim" method, this book provides a step-by step, active-participation approach to a clinical review of the medical literature.

In a way, this book is a traditional book on research methods turned upside down. Instead of telling the researcher how to do and report a study, this book tells the reader how to apply such knowledge in determining the quality of a medical paper. As such it is well written, clear and relevant.

I would like to suggest, however, that research methods are always depending on a views in the philosophy of science. (Courses in the Philosophy of Medicine are becoming more and more important in the education of Doctors). As an example of a well received book I can mention "Philosophy of Medicine. An Introduction" by Henrik R. Wulff, Stig Andur Pedersen & Raben Rosenberg (Oxford, UK: Blackwell Scientific Publications, 1986). Knowledge of this kind should enable the reader to read and interpret the medical literature at a still higher level. (It is of course more difficult to write easy "how to read books" based on such a more theoretical and philosophical level compared to the more statistical and methodical level).

There exists a broader literature on "Clinical reasoning in the health Professions" (Higgs et al), "Medical semiotics" (Baer et al.), diverse philosophical studies of medicine, "Quality in science" and much more. It would be interesting if anyone would try to expose how such knowledge could be turned upside down as guide on how to read the medical literature on a still deeper level.

It is my claim that a general background in philosophy and science studies should provide readers with even better qualifications to read the scientific literature. This is not an extraordinary position. In Denmark courses in the philosophy of science are very popular, and just now are we discussing to make such courses compulsory in all university studies.