{'fraction_non_alphanumeric': 0.023029866858582222, 'fraction_numerical': 0.00791651673263764, 'mean_word_length': 5.303854875283447, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Review of: “Testing Statistical Hypotheses of Equivalence and Noninferiority, Second Edition, by S. Wellek”

Stefan Wellek has updated his 2003 text on testing statistical hypotheses of equivalence. The first edition was well received (Holmgren, 2004). The update is timely and includes some material on new subjects, as well as updates of material presented in the original edition. The second edition is arranged as follows. After an introductory chapter, chapter 2 discusses testing non-inferiority and chapter 3 discusses generalities around testing equivalence. The next five chapters discuss equivalence tests in studies of different designs: one sample (chapter 4), paired sample (chapter 5), independent sample (chapter 6), multiple sample (chapter 7), and multivariate data (chapter 8). Chapter 9 discusses tests for goodness of fit, chapter 10 discusses bioequivalence, and chapter 11 discusses demonstrating relevant differences between treatments. The appendices contain much of the details of the mathematical derivations and some computer code. Within the various chapters are discussions of tests that assume distributions (especially normality), tests of binary and categorical data, and rankbased tests. The primary change from the first edition, evident from both the table of contents and the title, is the addition of information on testing of noninferiority. As noninferiority is a subject that could easily merit a separate text, the author was challenged to balance the need for comprehensive treatment with the need for concise writing. The result is a brief discussion on the mathematical foundation of noninferiority testing. Discussion of noninferiority analyses of paired observation designs and Bayesian treatments of noninferiority is included, giving an idea of the author’s intent to be inclusive of many designs and analyses of noninferiority trials. Despite the brevity of the section devoted specifically to noninferiority, much more of the book is relevant to clinical trials of noninferiority due to the vast overlap of many aspects of noninferiority testing and equivalence testing. The author includes some discussion of tests aimed at demonstrating lack of an important effect, other than difference in the means of treatments. Examples of this include chapter 9 on goodness of fit tests, and a section in chapter 6 on testing for equivalence of variances. For many researchers (whether trained in advanced statistics or not) it is standard to use the assumption that the data fit the model as the null hypothesis, and to reject that assumption only if the data are strongly inconsistent with it. Of concern with this strategy, of course, is that the test could be underpowered to find an important departure from that assumption. Wellek