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This book provides an up-to-date review of the general principles and techniques of confirmatory adaptive designs, a generalization of group sequential designs. With these designs, interim analyses are performed in order to stop a trial prematurely under control of the Type I error rate. In adaptive designs, it is also permissible to perform a data-driven change of relevant aspects of the study design at interim stages. This includes, for example, a sample-size reassessment, a treatment-arm selection, or a selection of a pre-specified sub-population.
First introduced in the 1990s, this popular adaptive methodology has become the focus of intense discussion and is still a rapidly growing field of statistical research. The book describes adaptive design methodology at an elementary level, while also considering design and planning issues. It also looks at methods for analyzing an adaptively planned trial, such as estimation methods and methods for determining an overall p-value. Part I provides the group sequential preliminaries required to understand and apply the adaptive design methodology supplied in Parts II and III. Many examples are included that illustrate the practical applications of the techniques. An overview of recent developments is given and, new to this edition, detailed descriptions of the R commands used for the calculations are provided. The R package rpact, which is available on CRAN, allows for the recalculation of most tables and results presented in the monograph. The required knowledge of R has been kept to a minimum, and an online Shiny app has been made available for rpact.
Primarily written for applied statisticians from academia and industry who are interested in confirmatory adaptive designs, the text is also suitable for an advanced statistical course for applied statisticians or clinicians with a sound statistical background.
Sommario
Part I Group Sequential Designs.- Repeated Significance Tests Procedures with Equally Sized Stages.- Procedures with Unequally Sized Stages.- Confidence Intervals, p -Values, and Point Estimation.- Applications.- Part II Adaptive Confirmatory Designs with a Single Hypothesis: Adaptive Group Sequential Tests.- Decision Tools for Adaptive Designs.- Estimation and p-Values for Two-stage Adaptive Designs.- Adaptive Designs with Survival Data.- Part III Adaptive Designs with Multiple Hypotheses: Multiple Testing in Adaptive Designs.- Applications and Case Studies.- Appendix - Software for Adaptive Designs.- Index.
Info autore
Gernot Wassmer is Adjunct Professor of biostatistics at the Institute of Medical Statistics, University of Cologne, Germany. He received his Ph.D. from the University of Munich, Germany, in 1993, after which he was Research Fellow at Munich’s Institute of Medical Statistics, at the Institute for Epidemiology, GSF Neuherberg, and at the Institute of Medical Statistics, University of Cologne. His major research interest is in the field of statistical procedures for group sequential and adaptive plans in clinical trials. He has been Member of independent data monitoring committees for international, multi-center trials in various therapeutic fields and also serves as Consultant for the pharmaceutical industry.
Werner Brannath is Professor of biostatistics at the Faculty of Mathematics and Informatics, University of Bremen, where he is also Head of the biometry group at the Competence Center for Clinical Trials. He has extensive experience in the planning and analysis of clinical trials and has been Member of several independent data monitoring committees. His main research interests include adaptive and group sequential designs, as well as multiple testing.
Riassunto
This book provides an up-to-date review of the general principles and techniques of confirmatory adaptive designs, a generalization of group sequential designs. With these designs, interim analyses are performed in order to stop a trial prematurely under control of the Type I error rate. In adaptive designs, it is also permissible to perform a data-driven change of relevant aspects of the study design at interim stages. This includes, for example, a sample-size reassessment, a treatment-arm selection, or a selection of a pre-specified sub-population.
First introduced in the 1990s, this popular adaptive methodology has become the focus of intense discussion and is still a rapidly growing field of statistical research. The book describes adaptive design methodology at an elementary level, while also considering design and planning issues. It also looks at methods for analyzing an adaptively planned trial, such as estimation methods and methods for determining an overall p-value. Part I provides the group sequential preliminaries required to understand and apply the adaptive design methodology supplied in Parts II and III. Many examples are included that illustrate the practical applications of the techniques. An overview of recent developments is given and, new to this edition, detailed descriptions of the R commands used for the calculations are provided. The R package rpact, which is available on CRAN, allows for the recalculation of most tables and results presented in the monograph. The required knowledge of R has been kept to a minimum, and an online Shiny app has been made available for rpact.
Primarily written for applied statisticians from academia and industry who are interested in confirmatory adaptive designs, the text is also suitable for an advanced statistical course for applied statisticians or clinicians with a sound statistical background.
