Fundamentals of Biostatistics PDF

Rating: (14 reviews) Author: Bernard Rosner ISBN : 9780538733496 New from $134.98 Format: PDF

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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.

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• Product Details
• Table of Contents
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• Hardcover: 888 pages
• Publisher: Cengage Learning; 7 edition (August 19, 2010)
• Language: English
• ISBN-10: 0538733497
• ISBN-13: 978-0538733496
• Product Dimensions: 1.5 x 7.9 x 10 inches
• Shipping Weight: 3.8 pounds (View shipping rates and policies)

Fundamentals of Biostatistics PDF

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.

I grew frustrated with this book and sought out other ones, but in the end I returned to this book.

I found it more thorough than alternatives, but grew frustrated with a few things, one in particular: the author's habit of referring to previous sections, equations, and examples by their numbers only while providing no clues or context as to what those past sections, equations, and examples were about.

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 go on to 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. This means that there is an exorbitant amount of flipping back and forth in this book.

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 (was it the cancer example? the smoking one? vitamin A? you now have to flip back and retrace your steps).

I just don't feel like I have to do this so much with other textbooks. It grew ridiculously frustrating.

Also, the author explains things in a very complicated way. Sometimes I am so puzzled by his explanations that I look it up in another book, only to find that it's actually a simple concept that I already know!