Nonparametric Inference For Interval Censored Data
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The Statistical Analysis of Interval-censored Failure Time Data
Author | : Jianguo Sun |
Publisher | : Springer |
Total Pages | : 310 |
Release | : 2007-05-26 |
Genre | : Mathematics |
ISBN | : 0387371192 |
This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions.
Interval-Censored Time-to-Event Data
Author | : Ding-Geng (Din) Chen |
Publisher | : CRC Press |
Total Pages | : 435 |
Release | : 2012-07-19 |
Genre | : Mathematics |
ISBN | : 1466504250 |
Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.
Nonparametric Statistical Methods For Complete and Censored Data
Author | : M.M. Desu |
Publisher | : CRC Press |
Total Pages | : 392 |
Release | : 2003-09-29 |
Genre | : Mathematics |
ISBN | : 9781584883197 |
Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the general theory of statistical inference and introduce the concepts intuitively for students with minimal backgrounds. Derivations and mathematical details are relegated to appendices at the end of each chapter, which allows students to easily proceed through each chapter without becoming bogged down in a lot of mathematics. In addition to the nonparametric methods for analyzing complete and censored data, the book covers optimal linear rank statistics, clinical equivalence, analysis of block designs, and precedence tests. To make the material more accessible and practical, the authors use SAS programs to illustrate the various methods included. Exercises in each chapter, SAS code, and a clear, accessible presentation make this an outstanding text for a one-semester senior or graduate-level course in nonparametric statistics for students in a variety of disciplines, from statistics and biostatistics to business, psychology, and the social scientists. Prerequisites: Students will need a solid background in calculus and a two-semester course in mathematical statistics.
Nonparametric Inference
Author | : Z. Govindarajulu |
Publisher | : World Scientific |
Total Pages | : 692 |
Release | : 2007 |
Genre | : Mathematics |
ISBN | : 981270034X |
This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area.With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.
Some Inference Problems for Interval Censored Data
Author | : Tingting Yi |
Publisher | : |
Total Pages | : 130 |
Release | : 2005 |
Genre | : Regression analysis |
ISBN | : |
Analysis of Censored Data
Author | : Hira L. Koul |
Publisher | : IMS |
Total Pages | : 310 |
Release | : 1995 |
Genre | : Censored observations (Statistics) |
ISBN | : 9780940600393 |