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Introductory biostatistics (2nd ed.)
Le C., Eberly L., Wiley Publishing, Hoboken, NJ, 2016. 616 pp. Type: Book (978-0-470905-40-1)
Date Reviewed: Apr 19 2019

Le and Eberly’s Introductory biostatistics is in fact more than introductory. Not only do the authors painstakingly clarify the fundamentals of the topic, but they also provide sufficient material for an advanced undergraduate or postgraduate course in biostatistics. The text itself exceeds 500 pages (barring the references, answers to selected exercises, and index), but never mind: the length of this excellent book reveals the breadth of the topics addressed.

The book has 14 chapters. Chapters 1 and 2 introduce readers to the descriptive methods for categorical and continuous data, respectively. The subtopics include proportions, rates, and ratios (chapter 1) and tabular and graphical methods, numerical methods, and correlation coefficients (chapter 2). Chapter 3 is “Probability and Probability Models,” followed by chapter 4, “Estimation of Parameters,” where both point estimation and interval estimation are discussed with helpful illustrations. The fundamentals of statistical tests of significance are introduced in chapter 5, which is followed by a full chapter on population proportions in chapter 6 (the subsections include “One-Sample Problem with Binary Data,” “Analysis of Pair-Matched Data,” “Comparison of Two Proportions,” “Mantel-Haenszel Method,” and so on). In chapter 7, “Comparison of Population Means,” both parametric (for example, t-test) and nonparametric (for example, the Wilcoxon signed-rank test) tests are discussed, as well as one-way analysis of variance (ANOVA). Higher-order ANOVA in both factorial and block designs are addressed in chapter 8.

The authors would do well to split the book into two parts, with Part 1 ending with chapter 8. From here they divert to topics like regression analysis in chapter 9, covering both simple and multiple regression, followed by logistic regression in chapter 10, methods for count data in chapter 11, and repeated measure analysis in chapter 12. The last two chapters (13 and 14) are on survival analysis and study designs. The last chapter lucidly deals with two important aspects of biostatistics: clinical trials and sample size determination.

Significant features of the second edition include:

  • The incorporation of a new chapter on higher order ANOVA in factorial and block designs;
  • The incorporation of a new chapter on repeated measure analysis;
  • Examples using both R and SAS software;
  • Multiple additional exercises with hints and conceptual suggestions;
  • “Notes on Computations” sections provide useful insight into the use of statistical software; and
  • A website of the datasets used in the book.

Nowadays, several universities offer biostatistics as a specialization course in a master’s program. Although I would love to see a chapter on statistical genetics in the next edition, which would definitely lend this book a sense of completeness, this is nevertheless a decent effort by the authors to produce a biostatistics textbook for advanced undergraduate and postgraduate students.

More reviews about this item: Amazon, Goodreads

Reviewer:  Soubhik Chakraborty Review #: CR146539 (1906-0214)
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