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Book Review: S.-C. Chow and M. Chang 2007: Adaptive Design Methods in Clinical Trials

Adaptive Design Methods in Clinical trials by S.-C. Chow and M. Chang is one of two new books on adaptive clinical trials designs that appeared since last year. It covers a wide range of topics and includes chapters on adaptive randomization methods, adaptive sample size adjustments, adaptive seamless phase II/III designs, Bayesian optimal adaptive designs, and clinical trial simulation. The literature and methods presented in the book are status quo and some of the topics are hardly found in other books. The book starts with a general introduction (Chapter 1) including a definition, major statistical and practical issues, as well as regulatory perspectives of adaptive designs. Chapter 1 also introduces the problem of a changing study population in a clinical trial with protocol amendments. This issue is extensively discussed in Chapter 2, where methods to estimate and account for such population changes are developed. Little work has been done on this topic and the authors focus on their own methodological research. Since the development is recent, practical experience is supposed to be limited, but would be helpful for understanding the utility and acceptability of the approach. Chapter 3 deals with adaptive randomization methods and covers conventional randomization (including cluster randomization), treatment-adaptive, covariate-adaptive, and response-adaptive randomization tools. It also has a brief discussion on issues with adaptive randomization, for example, accrual bias, accidental bias, and selection bias, inference and power/sample size calculations for clinical trials. Chapter 4 on ‘adaptive hypotheses’ briefly mentions examples of adaptations that are accompanied with a change of the null hypotheses, particularly, the switch from the superiority to a noninferiority hypothesis. The main part of Chapter 4 deals with the choice of the noninferiority margin in a noninferiority trial (e.g., based on historical data) and describes a method that has been suggested by the authors recently. The topic appears relevant mainly for conventional noninferiority trials and, in light of the title of the chapter and the introductory section one would have expected a discussion of the application of the approach to trials that are initially designed for superiority and where the hypothesis is switched to noninferiority at an interim analysis. Such trials are considered problematic in the EMEA document ‘Points to consider on switching between superiority and non-inferiority’ (http://www.emea.europa.eu/pdfs/ human/ewp/048299en.pdf) because of the lack of a pre-defined noninferiority margin (and for other reasons as well). Chapter 5 on adaptive doseescalation studies gives a brief review of relevant literature and then discusses a hybrid frequentistBayesian adaptive dose-escalation/reassessment approach developed by the authors. Chapter 6 deals with group sequential designs and includes a discussion of early efficacy and futility stopping via common boundaries like the Pocock, O’Brien and Fleming and Wang and Tstiatis boundary classes and the error spending function approach of Lan and DeMets. Another topic is a group sequential design method where the early stopping and final test decision criteria are based on independent p-values computed from the incremental data accrued between the analysis time points. The chapter includes two trial examples, a discussion of the common activities and decision principles of Data Monitoring Committees, and a brief introduction to the concept of conditional power. It concludes with a section on practical issues. Chapter 7 considers adaptive sample size adjustment. It covers an introduction to methods for masked sample size reviews, has sections on unmasked sample size reassessments with the Cui–Hung–Wang method, the Bauer and Koehne Fisher product test, the inverse normal method of Lehmacher and Wassmer, and an extension of the authors’ independent p-value approach. Tests based on the (weighted) sum of independent p-values are considered as well. There is also a section on the conditional error function principle of Proschan and Hunsberger. The Müller and Schäfer approach is also mentioned in a very short section. Chapter 8 contains an informal discussion on various seamless Phase II/III designs with a numerical comparison of some approaches (focusing only on tests for the global null hypothesis) and a more detailed exposition of Sampson and Sill’s [1] drop-the-loser design. I found the latter exposition interesting, but very hard to read, mainly because the notation does not seem fully defined. Chapter 9 considers the problem of analyzing time till event data in a clinical trial where patients can switch