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Biostatistics For Dummies [Kindle Edition]

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Product Description

Product Description

Score your highest in biostatistics

Biostatistics is a required course for students of medicine, epidemiology, forestry, agriculture, bioinformatics, and public health. In years past this course has been mainly a graduate-level requirement; however its application is growing and course offerings at the undergraduate level are exploding. Biostatistics For Dummies is an excellent resource for those taking a course, as well as for those in need of a handy reference to this complex material.

Biostatisticians—analysts of biological data—are charged with finding answers to some of the world's most pressing health questions: how safe or effective are drugs hitting the market today? What causes autism? What are the risk factors for cardiovascular disease? Are those risk factors different for men and women or different ethnic groups? Biostatistics For Dummies examines these and other questions associated with the study of biostatistics.

  • Provides plain-English explanations of techniques and clinical examples to help
  • Serves as an excellent course supplement for those struggling with the complexities of the biostatistics
  • Tracks to a typical, introductory biostatistics course

Biostatistics For Dummies is an excellent resource for anyone looking to succeed in this difficult course.

From the Back Cover

Learn to: Understand key statistical concepts as they relate to biological sciences Interpret biological and statistical data in any setting Score your highest in your biostatistics course Baffled by biostatistics? Biostatisticians are charged with finding answers to some of the world's most pressing health questions: How safe or effective are drugs hitting the market today? What causes autism? What are the risk factors for cardiovascular disease? Covering the most relevant topics you'll encounter in a biostatistics course, Biostatistics For Dummies gives you plain-English explanations of important concepts and plenty of examples. Back to the basics — get up to speed on math and statistics concepts, find advice on selecting statistical software, and get an overview of clinical research The deal with data — find out how to collect data properly, summarize it concisely, display it in tables and graphs, and describe its qualities Size it up — grasp the most common statistical techniques for comparing groups: t tests, ANOVAs, chi-square tests, and Fisher Exact tests Let's regress — learn how to test for and quantify the relationship between two or more variables, from a simple straight-line regression to multiple, logistic, nonlinear, and other kinds of regression Survive and thrive — see how to calculate survival curves, test for a difference in survival between two or more groups of subjects, and apply the methods of regression analysis to survival data Open the book and find: Basic math and statistical formulas, concepts, and techniques you need to know The big picture of clinical research How to summarize and graph data The scoop on accuracy, precision, standard errors, and confidence intervals Ways to compare groups Common distribution functions Simple rules for sample-size calculations

Product details

  • Format: Kindle Edition
  • File Size: 3619 KB
  • Print Length: 408 pages
  • Publisher: For Dummies; 1 edition (10 July 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • Text-to-Speech: Enabled
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  • Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #460,417 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
1.0 out of 5 stars Quite technical, not really for dummies 27 July 2014
Format:Paperback|Verified Purchase
Not the simple straight forward english explainations im afraid. Quite technical, relies on the reader having some previous knowledge of the subject.
If youre looking for something that has the best of both worlds i.e. Simple & as in depth as you want it to be " biostatistics, the bare essentials" bu norman & streiner is the way forward
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5.0 out of 5 stars Does what it says on the tin 26 Oct. 2013
By Grace
Format:Paperback|Verified Purchase
Thanks to this book I felt the most confident in my last exam than I have ever done before. Thanks John!
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Most Helpful Customer Reviews on (beta) 4.4 out of 5 stars  29 reviews
10 of 10 people found the following review helpful
4.0 out of 5 stars Great non-technical introduction 30 Sept. 2013
By I Teach Typing - Published on
Format:Paperback|Vine Customer Review of Free Product (What's this?)
I have been teaching biostatistics for more than a dozen years and I am always looking for a book that is readable, thoughtful and not too technical for a math phobic audience. This book does very well on all three criteria and will now be on my short list with Biostatistics: The Bare Essentials, 3e and Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking. The prose is clear and the for dummies margin icons for important/dangerous/etc topics really helps to make this an easy and fast read. The book is not too shallow in the topics that are covered. I was not pleased when I saw Bayesian statistics were missing from the index but those ideas are mentioned as web bonus material. There is a bit of mathematical notation early on but readers who truly hate it will be able to read around it without loosing the gist.

The one real shortcoming of the book is the lack of a cohesive introduction to any statistical package. All the major packages (SAS, R, SPSS, etc) are introduced and many of the less expensive options (including web resources) are mentioned. The coverage of the packages are balanced (but the comments on SAS are not entirely true). The book would be much stronger with a web compendium that explains how to do all the common analyses in the common packages.

The book does an excellent job of encouraging people to think about data, as opposed to just doing math. So, the reader can look forward to gaining insights instead of just learning formulas. Suppliment this with a solid book on SAS like Common Statistical Methods for Clinical Research with SAS Examples, Third Edition or for R grab A Beginner's Guide to R (Use R!) and you will be well on your way.
9 of 10 people found the following review helpful
3.0 out of 5 stars If you already know how to work the formulas, then this book will provide a good summary of how biostatistics is organized. 30 Aug. 2013
By Tom Brody - Published on
Format:Paperback|Vine Customer Review of Free Product (What's this?)
BIOSTATISTICS FOR DUMMIES is a 390 page book on biostatistics where the emphasis is on clinical trials. From my own experience as a medical writer, I know that Clinical Study Protocols, Clinical Study Reports, Investigator's Brochures, and so on, make frequent use of statistical concepts and formulas such as, alpha value, type I error, type II error, Kaplan-Meier plots, Z statistic, t statistic, P value, confidence interval, standard deviation, and others. Most of these formulas are described in this book. This book takes care to explain concepts that might be overlooked in other statistics textbooks, such as the fact that the determination of a P value, "is arbitrary, it depends on how much of a risk you're willing to take of being fooled by random fluctuations, that is, of making a Type I error. Over the years, the value of 0.05 has become accepted as a reasonable criterion for declaring significance" (page 43). This type of description provides a good background for the medical writer. For example, this morning I was reading a Clinical Study Protocol, which had been submitted with a SPA, where the P value for interim analysis was 0.002 and where the P value for analysis when the clinical trial was completed was 0.04. The book fails to provide guidance on how to solve any formulas in biostatistics. The book fails to include any problem sets, and it certainly does not include any answers. This book will not enable any novice to understand any statistical formulas. On the other hand, if you already understand biostatistics and know how to work the formulas by hand, then this book will be useful for tying all of the formulas together, and for providing a "big picture" of biostatistics. The book takes the same tactic as that taken by actor Robert Preston in THE MUSIC MAN. This tactic is as follows: If you just "think" about the task at hand, without receiving any training and without engaging in any hard work or practice, then you will somehow (magically) acquire skill in performing the task. Unfortunately, Robert Preston's advocated technique will never succeed in the real-world situation. In the same way, BIOSTATISTICS FOR DUMMIES cannot succeed.

BIOSTATISTICS FOR DUMMIES has a 12 page table of contents, which might be considered excessive for a book that is only 390 pages. At any rate, this is only a comment on style. One theme of this book, is statements about mathematics being frightening. These statements occur, e.g., on pages 9, 15, 17, 18, 20, 28, 31, 45, 51, 154, and 369. For example, the book states that the summation symbol "strikes terror into the hearts of so many people" (page 28). On page 51, the book states that, "there's just no good reason to put yourself through the misery of mind-numbing calculations and waste your precious time." On page 154, we read about, "a dizzying collection of tests with exotic-sounding names . . . enough to make your head spin." It is the case that this book avoids all calculations in order to avoid frightening the readers.

At any rate, many of the common formulas in biostatistics involve only high school arithmatic (adding, subtracting, multiplying, and dividing) and can easily be solved with a pencil and paper. I respectfully disagree with the notion that biostatistics can be learned or can be understood by any "teaching method" that refrains from actually working through the formulas. BIOSTATISTICS FOR DUMMIES does not contain a single worked formula. There are no working examples. There are no problem sets. Although many of the formulas are presented, the reader is not given any data for plugging into these formulas. There are no examples of solved problems. In my opinion, this book is better suited for people already with a practical working knowledge in biostatistics, who are interested in account of the "big picture," and who are interested in learning if there are any formulas or concepts that have been missed.

WHAT TO DO. I recommend the following books. I have not found any biostatistics book that provides working examples for all of the common equations, however, if you buy three or four of the following books, then your will be on your way to grasping the statistical concepts that are needed for medical writing for FDA submissions. The following compares some of the books that I have used, and from this account, you can choose the books that teach the indicated formulas.

KAPLAN-MEIER PLOTS (survival plots). This is about Medical Statistics 2nd ed. by Kirkwood and Sterne. Kirkwood (pages 272-286) discloses survival plots. Pharmaceutical Statistics by David S. Jones fails to disclose Kaplan-Meier curves. For this topic, I refer the reader to the excellent discussion in Dawson & Turner. But Lange (pages 221-244) is by far the best for this topic.

SAMPLE SIZE AND POWER CALCULATIONS. Kirkwood (pages 417-418) is the best of the books for sample size calculations, as Kirkwood provides a straightforward formula. The book by David S. Jones discloses sample size and power calculations on pages 172-179. The Jones narrative seems to come to a dead end on page 175, where delta is revealed as equaling 2.5 mL. But according to my calculations, delta should be 1.2 mL (50-48.8=1.2 mL). Lange (page 127) seems to lead to a dead end (since Lange fails to explain where the number -0.84 comes from).

FORMULA for CONFIDENCE INTERVAL FOR LARGE SAMPLES. Pages 60-63 of Kirkwood covers this topic, while pages 135-141 of Jones covers this topic. For two different treatments (study drug group and placebo group), Kirkwood's coverage is on page 67-70, and Jones' coverage is on pages 141-144.

FORMULA for CONFIDENCE INTERVAL FOR SMALL SAMPLES. Kirkwood's pages 53-55 is equivalent to pages 151-154 of Jones, for this simple formula. But Kirkwood's presentation of general information on Confidence Intervals (pages 50-53 of Kirkwood) is far superior to any presentation in Jones on this particular topic. Hence, I would recommend readers consult both Jones and Kirkwood for this formula.

FORMULA for HYPOTHESIS TESTING FOR LARGE SAMPLES. Kirkwood's treatment is on page 46-49, and 69, while Jones's treatment is on pages 87-125. Kirkwood has only two examples, but Jones has seven examples. In this respect, Jones is much better than Kirkwood. Both books teach us that once we have calculated the Z statistic, there are two things we can do with it. First, we can plug it into a table of STANDARD NORMAL DISTRIBUTION and get the P value (a probability). Second, we can compare it with a standard number, e.g., "1.96" for use in hypothesis testing, that is, for getting a yes/no answer (page 157-159 of Jones, page 244 of Rosner). To write this commentary, I needed to piece together information from several books. None of the books provides an organized stand-alone account of this formula.

FORMULA for HYPOTHESIS TESTING FOR SMALL SAMPLES. Kirkwood's treatment is on page 66, while Jones' treatment is on pages 168-178. These books teach that once we have the t statistic, there are two things we can do with it. First, we can plug it into a table (Table A4 of Kirkwood) to obtain a P value, and get a probability. Second, we can compare it to the "critical value of the t distribution" for hypothesis testing, and get a yes/no answer (Lange, page 101, 107). To write this commentary, I needed to piece together information from several books. None of the books provides an organized stand-alone account of this formula.

Lange provides a DECISION TREE for determining which statistics formula to use. This DECISION TREE is located on the inside front cover, but it does not come with any written commentary. Lange has the right idea, as far as efficacy in statistics teaching is concerned. But Lange does not go far enough.

I found a statistics book with this kind of decision tree. The book is: Daniel WW (2009) Biostatistics, 9th ed., John Wiley & Sons, Inc., Hoboken, NJ, p. 176. Another fine book is as follows. Norman GR, Streiner DL (2008) Biostatistics 3rd ed. B.C. Decker, Inc., Hamlton, Ontario, p. 35. Norman and Streiner know how to teach, and they warn students of the various ambiguities and inconsistencies found in other statistics books. The Daniel book, and the Norman & Streiner book, fill in the gaps where other statistics book are just plain confusing. I am still puzzled as to why many statistics books use such strikingly different approaches to using the Z statistic, and different approaches for plugging the Z statistic into a table to get the P value. Norman and Streiner is unique among all statistics books, in actually recognizing this inconsistency in the table that must be used, for plugging in the Z value.
3 of 3 people found the following review helpful
5.0 out of 5 stars A Great Help for Critical Appraisers 24 Oct. 2013
By Sheri Strite - Published on
Format:Kindle Edition|Verified Purchase
This book is an excellent resource for those of us who conduct critical appraisals of the medical literature to evaluate the reliability and clinical usefulness of studies. Many of us who evaluate medical research focus on bias, confounding and chance in our reviews and have to rely on biostatisticians when we need to evaluate statistical testing because most writing on the topic is extremely challenging if one has not had formal biostatistical training.

I was delighted to find this book. This book is a wonderful boon because it is written in a clear style with the goal of making difficult information accessible and understandable. I will now have a greater ability to evaluate statistical testing used in clinical trials and other medical research myself, and it will be easier for me to engage in a more knowledgeable and participatory fashion in biostatistical consults. I also appreciated the author's wit and charm, and I value the reassurance he provides along the way.

This book is useful to critical appraisers because it can help us understand why certain common statistical tests are used in studies. The book also provides a needed resource for answering questions about various tests. Chapter 3, for example, provides much of what readers might learn in a one- semester basic biostatistics course. The chapter clarifies the difference between probability and odds, explains p-values and confidence intervals, covers sampling issues and hypothesis testing, and explains parametric and nonparametric tests.

Students, clinicians and all health care professionals who carry out or evaluate clinical research will benefit from this book. This book will be a perfect companion resource to my own book on evaluating medical studies, and I will be recommending it to my students and others.
4 of 5 people found the following review helpful
4.0 out of 5 stars a clear, succinct, interesting, and instructive review of biostatistics 6 Nov. 2013
By Jojoleb - Published on
Format:Paperback|Vine Customer Review of Free Product (What's this?)
Clearly describes basic to intermediate concepts in biostatistics
High level material is presented in a friendly, accessible manner
Examples of how to use statistical methods are stated clearly
The For Dummies format is used effectively here
A great adjunct to a basic biostatistics course
Does a great job of describing how to implement statistical methods that can easily be applied using different (free and commercial) software packages

Not for the absolute beginner--a superficial grounding in basic statistics is necessary
Much ancillary information is found on the web and not included as an appendix
Little or no mathematical derivation of statistical terms
No problem sets

Biostatistics for Dummies, by John C. Pezzullo would be a great supplement for a basic course in biostatistics of a good refresher for anyone who wants to carefully read the biological or medical literature. The book is a great alternative to your standard dry and opaque biostatistics 101 textbooks.

Of note, this is not a course in very basic statistics. Pezzullo quickly reviews basic terms but assumes that you have at least a passing knowledge of terms like mean, median, mode, standard deviation, standard error, Student's t-test etc. Pezzullo assumes that you aren't going to remember very much from your basic-statistics past and quickly refamiliarizes his readers with these terms. However, if you've never seen any of these terms before the pace of the book will seem fast and furious.

Pezzullo is clearly a seasoned professor of biostatistics. He is clearly able to describe basic to intermediate topics in biostatistics in language that is transparent, simple, and easy for the reader to understand.

Pezzullo effectively uses the 'For Dummies' format. He keeps the writing simple and uses the 'For Dummies' symbols to highlight 'technical stuff' that you might want to skip, 'remember' flags for data that is very important, pointing out 'tips,' and 'warnings' to point out pitfalls. I think it is probably impossible to write a rollicking, fun-to-read statistics textbook on statistics, but this is the closest thing coming. Pezzullo has a jaunty style and never forgets that this is first and foremost a how-to manual; he never lets you get bogged down in jargon but somehow manages to never talk down to his reader. This is a fine line to tread and Pezzullo does it well.

The book is broken up into 6 major parts. Part I covers biostatistics is a short overview of the subject, a review of major software packages (from free to expensive), and a great birds-eye view of what to expect from the rest of the book. Part II looks at summarization of data, accuracy, precision, and a first look at confidence intervals. At first, I thought that the first two parts, which span the first 152 pages, took a long time. After reading on, however, the information in these first chapters is quite crucial to a better understanding of the following parts of the book.

Part III looks at basic comparisons of groups, cross tabulations, fourfold tables, incidence and prevalence, and trials that show non-inferiority/equivalence rather than superiority. This is where the book starts to feel more like a biostatistics book and you start getting into major comparisons.

Part IV delves into correlation and regression analysis. This is the basic meat-and-potatoes number crunching that most people think of when they think of taking a biostatistics course. This is also where things often start to get more complicated and dicey for most reader. Pezzullo eschews any complicated formulas or mathematics and concentrates on which statistical methods apply to which kind of data and clearly shows the reader when and where to use a specific statistic.

Part V covers survival data. He starts with the Kaplan-Meier method and then goes beyond this. This part is vital to understanding outcomes in any study that involves survival analysis and is incredibly helpful in terms of understanding what test to use when comparing survival between two group or more groups.

Part VI is more of a bonus part. It looks at 'ten distributions worth knowing.' This covers the normal distribution, log-normal distribution, poisson distribution, etc. The descriptions are not specifically deep. Rather, Pezzullo's intent is to consolidate the reader's understanding of these important distributions described earlier in the text. The last chapter covers ten ways to estimate the number of subjects needed for a study. Most were covered earlier in the book, but they are highlighted here for quick reference and clarity.

One gripe about the book is that there is a lot of ancillary information that is only accessible via the web. In days gone by, all this information (or at least a large portion of it) would have been found in an appendix. Web-based extras are definitely the wave of the future and save big bucks when it comes to printing. These links also gets you to go to the For Dummies website where you might be tempted to buy more For Dummies products or click on a banner that will put a few cents in the For Dummies coffers. Maybe I'm old fashioned and maybe this I'm just an old curmudgeon and this is my personal pet peeve. However, when I buy a book I want it to be complete.

Additionally, there is minimal to no derivation of many statistical terms and distributions that are used in the text. This is not necessarily a shortcoming--some readers are put off by this kind of thing and only find it confusing. However, an appendix or web links (yes, this might be an appropriate topic for a web link) that covered even the more complex derivations would be welcome. Once a reader comes to terms with how to use various statistical methods, it is often helpful to at least see how they are derived. This can lead to a deeper appreciation for statistics in general and a better understanding of how and when one would apply specific methods.

Finally, Pezzullo supplies good examples, but there are no problem sets. Statistics--and other scientific or mathematical concepts--often make sense when you read about them. However, it often takes practice with problem sets to really bring a point home. My guess is that Pezzullo feels that this is beyond the scope of the present book. His readers will either have this kind of practice in the context of a statistics course or in reviewing sample problems from a basic textbook in the field.

On the whole, however, this is a worthy text. This is the most accessible textbook on biostatistics that I have ever read and Pezzullo has a real knack for describing things in a clear, succinct, and interesting manner. Recommended.
1 of 1 people found the following review helpful
4.0 out of 5 stars Clearly written Intro/Overview to Biostats for those new to the field 31 Oct. 2013
By H. Sapiens - Published on
Format:Paperback|Vine Customer Review of Free Product (What's this?)
Summary: if you need a book to determine which stats applies in what situations, this book provides a fast reference with clear explanations.

Disclaimer: I'm a scientist that works in clinical trials with lots of very large datasets. At one time, I worked for a large academic hospital where we had biostatisticians on hand to do the hard stats for us, but alas I moved to industry and now...a lot of the time I need to do the heavy lifting myself. So...I'm not a student and I am using stats for very specific questions.

Book is clearly laid out
Text is easy to follow and examples are given
Graphs are clear and illustrate particular points
The icons (like warning!) catch your eye and make important points easy to find
Section on understanding multiple tests (e.g. Welch or Mann-Whitney) are very well-written

Content barely scratches the surface of biostats - there is literally one paragraph on understanding false discovery rates (FDR) (pretty important stuff with large multiplexed data like flow cytometry or genomic data)
While the book is good at explaining what a formula is for, it does not explain how to apply it

Bottom line: I would not recommend this book outright - rather with caveats. It is good for orientating someone to the subject (an absolute novice) or as a quick reference for determining which test to apply when you already understand how to apply the formulas (e.g. a scientist or grad student). This book will not teach you biostats. This book is NOT for your average grad student in the biological sciences who need to understand how to handle multivariate data; it would be better suited for clinical sciences or bio types that have very discrete data sets and do not need to do a lot of correlations or understand FDR/controlling Type I errors when multiplicity is a concern. For what it does offer, I give it 4 stars - but overall it is lacking in depth and breadth.
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