Essentials Of Biostatistics In Public Health / Edition 2

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Essentials of Biostatistics in Public Health, Second Edition provides a fundamental and engaging background for students learning to apply and appropriately interpret biostatistics applications in the field of public health.

Many examples are drawn directly from the author’s remarkable clinical experiences with the renowned Framingham Heart Study, making this text practical, interesting, and accessible for those with little mathematical background. The examples are real, relevant, and manageable in size so that students can easily focus on applications rather than become overwhelmed by computations.
The text is accompanied by an online workbook: Statistical Computing Using Microsoft Excel (for Mac or PC).
New Features of the Second Edition:

• Learning objectives and more practice problems for every chapter
• A new chapter on survival analysis
• A new chapter on nonparametric statistics
• Coverage of sensitivity, specificity, and performance of screening tests connecting probability to real and important applications
• An expansion of the chapter on multivariable methods with more emphasis on interpretation of multivariable regression
• A bank of questions for the audience response system (“clickers”) for instructors

Looking for more real-life evidence? Check out Case 4 in Essential Case Studies in Public Health, Putting Public Health into Practice.

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Editorial Reviews

From The Critics
Reviewer: James C. Torner, MS, PhD(University of Iowa College of Public Health)
Description: This book presents the challenging topics of a traditional introductory biostatistics courses in an approachable manner to help students understand the quantification of health measures and their analysis. The access to the publisher's website is a plus.
Purpose: Designed to introduce analytical concepts and framework, the book exposes students to calculations and analytical problems. Workbooks available through the website use common software to facilitate learning.
Audience: This book is suitable for students beginning in public health. The material is presented at the applied level so undergraduate and graduate students in public health should be able to handle the book with basic algebra. Biostatistics students may not find the statistical depth they need. The written description of methods provides a suitable balance to the analytical methods.
Features: Basic study designs and measures of incidence and prevalence lay the foundation for data collection and description. The probability chapter covers basic concepts and does not put the material in context of application. The analysis chapters describe confidence intervals and hypothesis testing. The topics of power and sample size, multivariable methods, nonparametric testing, and survival analysis are not usually presented in beginning courses.
Assessment: The major highlights of this book are its readability and linkage of concepts, methods, and examples. Examples draw on a few studies such as the Framingham Heart Study to illustrate methods. There are many study guides and introductory biostatistics and epidemiology books, this one provides added value with its format, topics, and linkage to the publisher site with added materials such as workbooks, flashcards, and videos. This book should provide students with the basic competency they need.
From The Critics
Reviewer: Li C Liu, PhD (University of Illinois at Chicago School of Public Health)
Description: This book aims to provide fundamental biostatistical skills for public health students. The author presents statistical techniques and methods focusing on hand calculations, using example data from one real public health study, the Framingham Study.
Purpose: The purpose is to "provide a fundamental and engaging background for students learning to apply and appropriately interpret biostatistical application in the field of public health." With a few exceptions, the book does cover most important basic topics in biostatistics and has the potential to become a good textbook for an introductory biostatistics class. The author notes in the preface that the book focuses on the mastery of "by-hand" computations. On this, I do not agree with the author. Although mastering fundamental theories is very important, and hand calculation is a good way of achieving that, it is important for students nowadays, even in an introductory class, to master at least one or two software applications to help them do the job. Lots of students will not have an opportunity to take other biostatistics classes after an introductory one. It is important to equip them with some exposure to software.
Audience: The author targets public health students and this audience, as well as medical students with a basic understanding of biostatistics, will find this book a good fit.
Features: Among the topics covered are basic study designs in public health studies, with a focus on disease prevalence and incidence; fundamental probability theories; confidence interval estimation; hypothesis testing; an introduction to power and sample size considerations in basic designs; and an introduction to multivariate methods. It is unique the way the author uses data from a real public health study throughout the book, which provides continuity and relevance. The summary tables of formulas at the end of each chapter are very helpful. The topics are presented at a very basic level and explained in a clear manner. Explanations of methodology and interpretation are detailed, which will be helpful for beginners. The book could be improved with some format changes. Although I admire the author's effort in explaining every step of problem solving in detailed text, readers might find all the text distracting and out of focus. And for someone who takes a class and/or uses the book as reference, it is hard to find the key steps for solving a problem. The author could arrange the solutions of problems so that the explanatory text and equations are separate. To improve readability, the author could use itemized lists in some instances. For example, in section 4.3.1, descriptive statistics for continuous variables, all of the summary measures are in lengthy paragraphs, making it difficult to locate key concepts. Simply using bold fonts to highlight important concepts would help readers locate necessary information. For example, to find definitions for standard normal distribution, standard error of an estimate, etc., one needs to read through pages of text. Treatment of a few topics could be improved as well. Chapter 5 on probability theory would flow better by introducing marginal and joint probabilities before introducing the concept of independence and conditional probability. Bayes' rule is an example of the use of all of the above concepts, and screening tests are examples of Bayes' rule. In the section on probability models, the important concepts of probability distribution function (PDF) and cumulative probability function should be included. Poisson distribution is another important population distribution worth including. The author includes sampling distributions in this chapter, a concept I feel is very different from population distributions. They should not be in the same chapter. In the chapter on confidence interval estimation, the author focuses on making the assumption of equal variance for two-sample tests. First, in the tables for the formulas, that assumption should be restated. Second, methods when that assumption is violated should be mentioned. In section 7.8, the ANOVA section, the test of equal factor level mean is only the first step in ANOVA. The section should not stop there. Further discussion of mean comparisons and multiple test procedures should complete this section. In the introduction of regression and logistic regression, the author includes only main covariate effects, which will give students the illusion that this is the way to do multivariate analysis. Since many students will never take another statistics course, interaction models, which are very important in correctly identifying covariate effects, should be mentioned.
Assessment: This book is suitable for basic level biostatistics course. It is very well organized and covers the important topics for this audience. The example data provide continuity and relevance for public health students. However, there are two other very good books in the field: Biostatistics: A Foundation for Analysis in the Health Sciences, 8th edition, Daniel (John Wiley & Sons, 2005) and The Practice of Statistics in the Life Sciences, Baldi and Moore (W. H. Freeman, 2009). Compared with these, Essentials of Biostatistics covers less material with a less clear style.
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Product Details

  • ISBN-13: 9781449623944
  • Publisher: Jones & Bartlett Learning
  • Publication date: 3/24/2011
  • Edition number: 2
  • Pages: 314
  • Sales rank: 87,427
  • Product dimensions: 8.40 (w) x 10.80 (h) x 0.80 (d)

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