Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints, i.e., measures that can replace or supplement other endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and skepticism. Surrogate endpoints are useful when they can be measured earlier, more conveniently, or more frequently than the "true" endpoints of primary interest. Regulatory agencies around the globe, particularly in the United States, Europe, and Japan, are introducing provisions and policies relating to the use of surrogate endpoints in registration studies. But how can one establish the adequacy of a surrogate? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately?
This book offers a balanced account on this controversial topic. The text presents major developments of the last couple of decades, together with a unified, meta-analytic framework within which surrogates can be evaluated from several angles. Methodological development is coupled with perspectives on various therapeutic areas. Academic views are juxtaposed with standpoints of scientists working in the biopharmaceutical industry as well as of colleagues from the regulatory authorities.
Tomasz Burzykowski is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Burzykowski has published methodological work on the analysis of survey data, meta-analyses of clinical trials, and validation of surrogate endpoints. He is a co-author of numerous papers applying statistical methods to clinical data in different disease areas (cancer, cardiovascular diseases, dermatology, orthodontics).
Geert Molenberghs is Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He serves as Joint Editor for Applied Statistics (2001-2004) and is President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society.
Marc Buyse founded the International Drug Development Institute in 1991. He is Past President of the International Society for Clinical Biostatistics, Past President of the Quetelet Society, and Past Board Member of the Society for Clinical Trials. He is currently the Executive Director of IDDI (International Drug Development Institute) and Associate Professor of biostatistics at the Limburgs Universitair Centrum, Center for Statistics, Diepenbeek, Belgium. He has published extensively in the fields of biostatistics and oncology. His research interests include meta-analysis, surrogate endpoints, statistical detection of fraud, and the design and statistical analysis of clinical trials.
From the reviews:
"A strength of this book is its comprehensive and up-to-date presentation of issues pertinent to the evaluation of surrgoate endpoints...This book makes an important contribution to the clinical trials literature..." Journal of Biopharmaceutical Statistics, 2006
"Many of the chapters deal with real-life data examples and studies involving surrogate outcomes, many written by authors who were directly involved in these studies...The editors have written nice background sections...until a more concise manuscript on this topic is written, this book will remain the most important resource for biostatisticians and researchers in this area." Debajyoti Sinha for the Journal of the American Statistical Association, December 2006
"This book is a reflection of the ongoing debate on the definition and use of surrogate markers...I see the book as an invitation to join the debate. There is much work to be done and reading the book might inspire many to participate. It will be useful for researchers in this and related fields, such as joint modeling of longitudinal and survival data and multivariate meta-analysis. The book is well organized, is a pleasure to read, and is very well documented with up-to-date references." Hans C. Van Houwelingen for Bioometrics, September 2006
The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book.
Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package.
The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.
Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data. Examines the problems caused by missing data, enabling a complete understanding of how to overcome them. Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism. Illustrated throughout with real-life case studies and worked examples from clinical trials. Details the use and implementation of the necessary statistical software, primarily SAS. Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.