Bøger i Practical Guides to Biostatistics and Epidemiology serien

The success of the Apgar score demonstrates the astounding power of an appropriate clinical instrument. This down-to-earth book provides practical advice, underpinned by theoretical principles, on developing and evaluating measurement instruments in all fields of medicine. It equips you to choose the most appropriate instrument for specific purposes. The book covers measurement theories, methods and criteria for evaluating and selecting instruments. It provides methods to assess measurement properties, such as reliability, validity and responsiveness, and interpret the results. Worked examples and end-of-chapter assignments use real data and well-known instruments to build your skills at implementation and interpretation through hands-on analysis of real-life cases. All data and solutions are available online. This is a perfect course book for students and a perfect companion for professionals/researchers in the medical and health sciences who care about the quality and meaning of the measurements they perform.

This book explains all aspects of mixed model analysis without mathematical jargon, so that non-statisticians can understand the basic principles, analyze their own data, and interpret the results with confidence. Worked examples are analyzed with STATA, and all datasets are available for download, equipping readers to replicate the methods.

This practical guide for readers with a minimal background in statistics shows how to analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The author introduces key methods of survival analysis that are also central to statistical analysis in general.

This is the only book to provide both a practical introduction to secondary data analysis and a list of major sources of secondary data in the United States. Entries for each data source include the focus of the data, years available, data collection process used, and directions about how to access the data and supporting materials.

Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.

Genomics is majorly impacting therapeutics development in medicine. This book contains up-to-date information on the use of genomics in the design and analysis of therapeutic clinical trials with a focus on novel approaches that provide a reliable basis for identifying which patients are most likely to benefit from each treatment. It is oriented to both clinical investigators and statisticians. For clinical investigators, it includes background information on clinical trial design and statistical analysis. For statisticians and others who want to go deeper, it covers state-of-the-art adaptive designs and the development and validation of probabilistic classifiers. The author describes the development and validation of prognostic and predictive biomarkers and their integration into clinical trials that establish their clinical utility for informing treatment decisions for future patients.

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests(TM), neural nets, support vector machines, nearest neighbors and boosting.