Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.

Initial public offerings (IPOs), or new listings of companies on stock exchanges, are among the most important form of finance and generate considerable attention and excitement. They are used to raise capital or to monetize investments by the early generation of venture capital and other private investors. They are increasingly international in scope and reach, especially with non-American firms offering on American stock exchanges. This handbook provides a comprehensive overview of why companies list on stock exchanges, how IPOs are regulated, initially valued, and their performance in the short and long run. The first part examines the economics of IPOs, and offers statistics and regulatory insights from the United States and other countries around the world. The volume then covers mergers versus IPOs, as well as reverse mergers and special purpose acquisition companies. Part III analyzes institutional ties in IPOs, including analysts, investment banks, auditors, and venture capitalists. The fourth section provides international perspectives on IPOs from a number of countries around the world. Part V discusses alternatives to IPOs, including private marketplaces, and crowdfunding. Reflecting the range of disciplines that analyze IPOs, the contributors come from the fields of finance, international business and management, economics, and law. The chapters cover the latest information on a range of fundamental questions that are of interest to academics, practitioners, and policymakers alike.

The Handbook of Models for Human Aging is designed as the only comprehensive work available that covers the diversity of aging models currently available. For each animal model, it presents key aspects of biology, nutrition, factors affecting life span, methods of age determination, use in research, and disadvantages/advantes of use. Chapters on comparative models take a broad sweep of age-related diseases, from Alzheimer's to joint disease, cataracts, cancer, and obesity. In addition, there is an historical overview and discussion of model availability, key methods, and ethical issues. - Utilizes a multidisciplinary approach - Shows tricks and approaches not available in primary publications - First volume of its kind to combine both methods of study for human aging and animal models - Over 200 illustrations

Biomedical and social science researchers who want to analyze survival data with SAS will find just what they need with this easy-to-read and comprehensive guide. Teaches many aspects of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, completely updated for SAS 9.

This title consists of 19 essays dealing with the medical knowledge and beliefs of cultures outside of the United States and Europe. In addition to articles surveying Islamic, Chinese, Native American, Aboriginal Australian, Indian, Egyptian, and Tibetan medicine, the book includes essays on comparing Chinese and western medicine and religion. the medical practices to the cultures which produced them. Each essay is well illustrated and contains an extensive bibliography. Because the geographic range is global, the book should fill a gap in both the history of medicine and in cultural studies. It should find a place on the bookshelves of advanced undergraduate students, graduate students, and scholars, as well as in libraries serving those groups.

The Moderator's Survival Guide is your indispensable resource for navigating the rocky shoals of your one-on-one user research sessions. Inside, you'll find guidance for nearly 100 diverse situations (ranging from business-as-usual to tricky and sticky) that might occur during usability studies, contextual inquiries, or user interviews. As a moderator, you are responsible for the well-being of the participant, your study, and your organization. You must be prepared for anything that may happen, from your technology failing to the participant quailing. Use this guide to identify your best next steps, react appropriately, and survive any challenges that comes your way. - Practical, field-tested, and actionable tips for what to do and say—and what NOT to do or say—in each situation. - Key patterns and extensive examples to sharpen your approach to the commonplace and prepare you for the unlikely. - Illustrative "survival stories" contributed by numerous professionals on the front lines of user research.

Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.