"The book is clearly set-out and easy to follow and each chapter ends with a summary and detailed list of references. … an excellent general text covering the whole spectrum of working with PROs from design of measures through to the analysis of PRO data."
—ISCB News, 59, June 2015
"The book is well written and well organized. Good examples contribute to a better understanding of statistics. The focus is quantitative and includes psychometric and statistical methods relevant to the vast majority of patient-reported outcome measures, those based on multi questions scales or summed assessment scales. The content is also relevant for individualized and preference-based instruments. ... The book is recommended for health care professionals and health researchers who wish to further their understanding of psychometric and statistical methods necessary for the development, testing and use of patient-reported outcome measures."
—A Garratt, Journal of the Norwegian Medical Association, April 2015
"This book includes 11 chapters on 330 pages. The first six chapters cover topics in psychometrics, and the remaining five cover topics in statistics. ... the book is a quick, easy, and understandable introduction to qualitative and quantitative psychometric methods for the inexperienced reader."
—John Brodersen, Statistics in Medicine, 2015
"The recent book, Patient-Reported Outcomes, Measurement, Implementation and Interpretation, by Cappelleri and his colleagues is a timely synthesis of the current thinking and practice of psychometrics applied to patient reported outcomes (PROs), and PRO data analysis. Written primarily by statisticians drawn from the same large multinational pharmaceutical company, the book is squarely aimed at health economists, outcomes researchers and statisticians in the industry who may require a foundation in psychometrics and the analysis of PRO data. That said it may also be of benefit and utility to advanced researchers in academia and consultancy wanting to increase their knowledge in this field. ... In summary, this book attempts to cover a lot of complex material and does so successfully. It effectively teaches core concepts related to the measurement, analysis and interpretation of PROs. There is something for readers of all levels of familiarity with PROs, from the novice to the more experienced researcher."
—Qual Life Res, 2014
"This is a timely publication that provides comprehensive information on an important subject. … there are many who can benefit from reading this book, including health care professionals, health services researchers, policy makers, regulators, patients, caregivers, and other health-related stakeholders. … This book includes many excellent simulated and real-life examples. … For many practitioners, the sample codes provided in the book provide an excellent template for them to carry out their own analyses. This book is well organized and clearly written. Accessible language and articulate writing make this a very readable book. Ample examples with step-by-step illustrations of the analysis of data and interpretation of the results will be particularly helpful to readers who are new to PROs. It can also serve as a valuable reference for those who have experience working with PROs."
—Journal of Biopharmaceutical Statistics, 2014
"… a clear and thorough presentation of key topics for understanding the development, validation, and scoring of patient-reported outcome (PRO) measures and for conducting the analysis of PRO data to explore drug effects and describe treatment benefits. … very readable. The text successfully integrates commonly used statistical methods such as reliability measures, longitudinal models, and missing data analysis into the context of PROs and highlights relevant issues. The authors’ expertise and knowledge of the field of PROs is evident in the presentation of each method … a very valuable and lasting contribution to the field of PROs. I highly recommend it as an introduction and guide to the analysis of PRO data because of its clarity, focus, and comprehensiveness."
—Clinical Trials, Vol. 11, 2014
