This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets. A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
The emphasis is on presenting practical problems and full analyses of real data sets. Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible.
The applications are all from the health sciences, including cancer, AIDS, and the environment. Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools.
Written with few details of S-PLUS and less technical descriptions, the book concentrates solely on medical data sets, demonstrating the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.
Author : David G. New material has been added to the second edition and the original six chapters have been modified. The previous edition sold copies world wide since its release in Based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Provides a "user-friendly" layout and includes numerous illustrations and exercises. Written in such a way so as to enable readers learn directly without the assistance of a classroom instructor.
Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets. Author : Vijay Nair Publisher: World Scientific ISBN: Category: Mathematics Page: View: Read Now » There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies.
These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods.
These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference. Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques.
With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners. The first part gives a largely non-mathematical introduction to data exploration, univariate methods including GAM and mixed modeling techniques , multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies. Data from all case studies are available from www. Guidance on software is provided in the book.
The previous edition sold copies world wide since its release in Based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Provides a "user-friendly" layout and includes numerous illustrations and exercises.
Written in such a way so as to enable readers learn directly without the assistance of a classroom instructor. Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets. Author : Vijay Nair Publisher: World Scientific ISBN: Category: Mathematics Page: View: Read Now » There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies.
These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods.
These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference.
Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.
The first part gives a largely non-mathematical introduction to data exploration, univariate methods including GAM and mixed modeling techniques , multivariate analysis, time series analysis, and spatial statistics.
The second part provides 17 case studies. Data from all case studies are available from www. Guidance on software is provided in the book. Author : Ruth M. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk.
Features: Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate models Provides details on statistical inference for the various sampling designs Discusses criteria for evaluating risk models and comparing risk models, including both general criteria and problem-specific expected losses in well-defined clinical and public health applications Describes many applications encompassing both disease prevention and prognosis, and ranging from counseling individual patients, to clinical decision making, to assessing the impact of risk-based public health strategies Discusses model updating, family-based designs, dynamic projections, and other topics Ruth M.
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