I enjoyed reviewing the new CRC Press/Chapman Hall book entitled Power Analysis of Trials with Multilevel Data, by Mirjam Moerbeek and Steven Teerenstra. This book addresses a critical need in the scientific community for a well-organized, easily accessible guide to performing power analysis and computing required sample sizes for randomized trials embedded in multilevel study designs, where observations of interest are nested within higher level units (e.g. patients within clinics or repeated measures on participants). ...
This book effectively compiles all the published literature on this specialized topic, putting it in one place for researchers who design these types of studies and could benefit from a concise and practical resource on this important aspect of study design. The two Dutch authors are experts in this area and are very well-equipped to provide more general education and practical advice on this topic. Multilevel study designs in which power analysis methods for independent observations do not apply are quite common, but no prior books have attempted to organize all the possible power analysis approaches for these types of studies into a single reference. ...
In sum, this will be a very useful book for researchers, statisticians, and consultants responsible for designing various types of randomized trials in multilevel settings. My minor quibbles are far outweighed by the important contributions that this single resource on power analysis in multilevel designs will make to the scientific community.
-Brady T. West, University of Michigan, Biometrical Journal, May 2017
...the appearance of the book, Power Analysis of Trials with Multilevel Data, is well timed...Another nice feature of the book is the example power analyses that conclude most chapters (and sometimes appear earlier in chapters as well). The authors have done a very good job finding articles in the literature that use a particular design, extracting relevant parameters from those articles, and then illustrating how to use those parameters to plan a replication study...I think this book deserves a place on the bookshelf of both researchers who plan experimental studies and statisticians who advise them.
-Christopher H. Rhoads, University of Connecticut, The American Statistician, November 2016
I enjoyed reviewing the new CRC Press/Chapman Hall book entitled Power Analysis of Trials with Multilevel Data, by Mirjam Moerbeek and Steven Teerenstra. This book addresses a critical need in the scientific community for a well-organized, easily accessible guide to performing power analysis and computing required sample sizes for randomized trials embedded in multilevel study designs, where observations of interest are nested within higher level units (e.g. patients within clinics or repeated measures on participants). ...
This book effectively compiles all the published literature on this specialized topic, putting it in one place for researchers who design these types of studies and could benefit from a concise and practical resource on this important aspect of study design. The two Dutch authors are experts in this area and are very well-equipped to provide more general education and practical advice on this topic. Multilevel study designs in which power analysis methods for independent observations do not apply are quite common, but no prior books have attempted to organize all the possible power analysis approaches for these types of studies into a single reference. ...
In sum, this will be a very useful book for researchers, statisticians, and consultants responsible for designing various types of randomized trials in multilevel settings. My minor quibbles are far outweighed by the important contributions that this single resource on power analysis in multilevel designs will make to the scientific community.
-Brady T. West, University of Michigan, Biometrical Journal, May 2017
...the appearance of the book, Power Analysis of Trials with Multilevel Data, is well timed...Another nice feature of the book is the example power analyses that conclude most chapters (and sometimes appear earlier in chapters as well). The authors have done a very good job finding articles in the literature that use a particular design, extracting relevant parameters from those articles, and then illustrating how to use those parameters to plan a replication study...I think this book deserves a place on the bookshelf of both researchers who plan experimental studies and statisticians who advise them.
-Christopher H. Rhoads, University of Connecticut, The American Statistician, November 2016
