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Fundamentals of Biostatistics (Rosner, Fundamentals of Biostatics), by Bernard Rosner
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FUNDAMENTALS OF BIOSTATISTICS leads you through the methods, techniques, and computations necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.
- Sales Rank: #42250 in Books
- Brand: Brand: Cengage Learning
- Published on: 2010-08-19
- Original language: English
- Number of items: 1
- Dimensions: 10.10" h x 1.50" w x 8.00" l, 3.74 pounds
- Binding: Hardcover
- 888 pages
- Used Book in Good Condition
About the Author
Bernard Rosner is Professor in the Department of Medicine, Harvard Medical School, and the Department of Biostatistics at the Harvard School of Public Health. Dr. Rosner's research activities currently include longitudinal data analysis, analysis of clustered continuous, binary and ordinal data, methods for the adjustment of regression models for measurement error, and modeling of cancer incidence data.
Most helpful customer reviews
39 of 41 people found the following review helpful.
Very good text for MPH students, but not perfect
By Bartell household
This is the second year that I've taught introductory public health statistics out of Rosner's book.
Pros:
* Good sections on probability and fundamentals of inference that show enough of the derivations to allow the better students to appreciate the mathematical basis, without making that the emphasis of the text.
* Includes a brief discussion of Bayesian inference and simple examples. This constitutes a very small fraction of the book, but is typically absent from introductory biostatistics texts.
* As much emphasis on poisson and binomial models as normal models. This is critical for public health students!
* Great section on epidemiologic methods in the final chapters, including logistic regression.
* Good coverage of exact methods and a short chapter on classical non-parametrics.
* Includes some discussion of missing data analysis.
* Hundreds of homework problems to choose from at the end of each chapter, with solution sets available online to registered course instructors.
Cons:
* The two-tailed p-value is always described as 2 times the one-tailed p-value, which is fine for symmetric sampling distributions but not so good for binomial and poisson distributions. This is a big source of confusion in my class, as it's inconsistent with R.
* An antiquated emphasis on calculating critical values for hypothesis tests, rather than using p-values or confidence intervals.
* The chapter on two-sample t-tests instructs students to assume equal variances if a formal hypothesis test for equal variances does not result in rejection of the null. This is ill advised and makes the entire chapter needlessly complicated.
* More emphasis than I'd like on normal approximations for binomial and poisson distributions and corresponding one-sample tests. Again, this seems to cause needless confusion considering that it is much simpler for the students to obtain exact results.
* Some of the example homework problems are not worded very clearly, so be careful about selection. This also leads to a few surprising interpretations/answers in the online solutions.
* Minor quibble: two-sample binomial tests are covered in two different sections of the book, depending on which version of the effect estimate is used (p2-p1, p2/p1, odds ratio, etc.). I wish they were all presented together.
26 of 28 people found the following review helpful.
Kindle Edition
By lsgrant
The Kindle edition is essentially useless. The equations don't show up and none of the tables show up correctly. I have homework in this class for which I have to use this book and I am EXTREMELY disappointed that I have to inconveniently commute back and forth to the library to photocopy multiple pages from the text--spending more money, which is what I was trying to avoid by having the Kindle edition. You should take this off as something you can purchase. I wouldn't even want it if it was free!
17 of 18 people found the following review helpful.
Good but frustrating to read
By Whitaker
I grew frustrated with this book and sought out other ones, but in the end I returned to this book, because it is more comprehensive and contains more examples. These are easily its biggest strengths. But there are two huge, immensely frustrating downsides:
1. The language is downright incomprehensible. It is overly technical; it is entirely math-speak; he will use symbols and equations over words any day, including in a regular paragraph. When he wants to explain a concept, he often does so by deriving it mathematically through a series of half a dozen equations instead of just explaining in plain english what the statistical test does. There are times when I'm exasperated from trying to read two dozen mathematical equations trying to understand his point, only to look in another textbook and realize it was something I already knew and to laugh -- he had literally made something I already knew completely unrecognizable.
2. The author's habit of referring to previous examples and equations by their numbers only, for example "Equation 6.14", without reminding you what they were about. Sometimes he refers to an example or equation all the way back in the previous chapter like this, like you were supposed to just have memorized "Equation 6.14". It requires you to flip back and forth a whole lot.
For example in the first two sentences of section 7.8 ... well into chapter 7 ... he writes, "One limitation of the methods of interval estimation in Section 6.5 is that it is difficult to make direct statements such as Pr(c1 < ' < c2) = 1 ' '. Instead, we have made statements such as Equation 6.7." Of course, he doesn't remind you what Equation 6.7 is. You're just supposed to remember equation 6.7 and section 6.5, which were in the previous chapter. He does this with examples, too, building on "example 6.1" without reminding you in the least what it was about or how far you've gotten on it.
But I returned to this book in the end and dealt with it because, once again, it is more comprehensive and provides more examples. But I tend to have a couple of other books on hand to make the job easier, e.g. one of those simplified statistics books and an alternate textbook. They rarely go into as much detail as Rosner but I pretty much have to read the relevant sections in those books first to even get the general idea of what Rosner is trying to explain.
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