Geert Molenberghs – författare

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 concepts of estimands, analyses (estimators), and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language, providing technical details, real-world examples, and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface, such as between causal inference and clinical trialists, as well as among clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence.

This book lays out a path toward bridging some of these gaps. It offers

A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges

A thorough treatment of intercurrent events (ICEs), i.e., postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)

Details on how estimands, integrated into a principled study development process, lay a foundation for coherent specification of trial design, conduct, and analysis needed to overcome the issues caused by ICEs:

A perspective on the role of the intention-to-treat principle

Examples and case studies from various areas

Example code in SAS and R

A connection with causal inference

Implications and methods for analysis of longitudinal trials with missing data

Together, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial and academic perspective.

The concepts of estimands, analyses (estimators), and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language, providing technical details, real-world examples, and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface, such as between causal inference and clinical trialists, as well as among clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence.

This book lays out a path toward bridging some of these gaps. It offers

A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges

A thorough treatment of intercurrent events (ICEs), i.e., postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)

Details on how estimands, integrated into a principled study development process, lay a foundation for coherent specification of trial design, conduct, and analysis needed to overcome the issues caused by ICEs:

A perspective on the role of the intention-to-treat principle

Examples and case studies from various areas

Example code in SAS and R

A connection with causal inference

Implications and methods for analysis of longitudinal trials with missing data

Together, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial and academic perspective.

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.

Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained volume, an outgrowth of an "International Conference on Statistics in Health Sciences," covers a wide range of topics pertaining to new statistical methods in the health sciences.

The chapters, written by leading experts in their respective fields, are thematically divided into the following areas: prognostic studies and general epidemiology, pharmacovigilance, quality of life, survival analysis, clustering, safety and efficacy assessment, clinical design, models for the environment, genomic analysis, and animal health.

This comprehensive volume will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

In today''s healthcare landscape, there is a pressing need for quantitative methodologies that include the patients'' perspective in any treatment decision.

Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis provides a comprehensive overview of an innovative and powerful statistical methodology that generalizes the traditional Wilcoxon-Mann-Whitney test by extending it to any number of outcomes of any type and including thresholds of clinical relevance into a single, multidimensional evaluation.

The book covers the statistical foundations of generalized pairwise comparisons (GPC), applications in various disease areas, implications for regulatory approvals and benefit-risk analyses, and considerations for patient-centricity in clinical research. With contributions from leading experts in the field, this book stands as an essential resource for a more holistic and patient-centric assessment of treatment effects.

In today''s healthcare landscape, there is a pressing need for quantitative methodologies that include the patients'' perspective in any treatment decision.

Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis provides a comprehensive overview of an innovative and powerful statistical methodology that generalizes the traditional Wilcoxon-Mann-Whitney test by extending it to any number of outcomes of any type and including thresholds of clinical relevance into a single, multidimensional evaluation.

The book covers the statistical foundations of generalized pairwise comparisons (GPC), applications in various disease areas, implications for regulatory approvals and benefit-risk analyses, and considerations for patient-centricity in clinical research. With contributions from leading experts in the field, this book stands as an essential resource for a more holistic and patient-centric assessment of treatment effects.

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory