Werner Brannath – författare

The COVID-19 pandemic has brought into sharp focus the need for the collection of data. Such data cannot be collected or provided without medical informatics, documentation and health data management. Nor can health data be evaluated and converted into a useful tool for making the important decisions that affect us all without biometrics and epidemiology, bioinformatics and systems biology.This book presents full papers from GMDS & CEN-IBS 2020, the first joint online conference of the German Association of Medical Informatics, Biometry and Epidemiology (GMDS) and the Central European Network & the International Biometric Society (CEN-IBS), held online between 6 and 11 September 2020. The title of the conference was Bringing Data to Life, a reference to the increasing amount of data in medical research which is inextricably related to the fast-developing digitalization of the health system. Many challenges must be addressed in order to make use of and benefit from these increasing sources of data, and these can only be faced if all disciplines related to data science work together. The conference aimed to bring together the diverse disciplines within data science, including medical informatics, bioinformatics, biostatistics, epidemiology, public health and medical documentation. Topics covered in the book include central themes relevant to society in general and advances in data technology which support innovations in medical research in particular.The book brings together many topics related to the provision and analysis of data in medicine, and will be of interest to all those working in the field.

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.

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.