Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Partial Linear Semi Parametric Additive Hazards Models For Randomly Censored Survival Time Data PDF full book. Access full book title Partial Linear Semi Parametric Additive Hazards Models For Randomly Censored Survival Time Data.
| Author | : Chaofeng Liu |
| Publisher | : |
| Total Pages | : 370 |
| Release | : 2002 |
| Genre | : |
| ISBN | : |
| Author | : Jianguo Sun |
| Publisher | : Springer Nature |
| Total Pages | : 322 |
| Release | : 2022-11-29 |
| Genre | : Mathematics |
| ISBN | : 3031123662 |
This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censored survival data can easily be mistaken for typical right-censored survival data, which can result in erroneous statistical inference due to the complexity of this type of data. The book invites a group of internationally leading researchers to systematically discuss and explore the historical development of the associated methods and their computational implementations, as well as emerging topics related to interval-censored data. It covers a variety of topics, including univariate interval-censored data, multivariate interval-censored data, clustered interval-censored data, competing risk interval-censored data, data with interval-censored covariates, interval-censored data from electric medical records, and misclassified interval-censored data. Researchers, students, and practitioners can directly make use of the state-of-the-art methods covered in the book to tackle their problems in research, education, training and consultation.
| Author | : Mikhail Nikulin |
| Publisher | : Springer |
| Total Pages | : 131 |
| Release | : 2016-04-11 |
| Genre | : Mathematics |
| ISBN | : 3662493322 |
This book will be of interest to readers active in the fields of survival analysis, genetics, ecology, biology, demography, reliability and quality control. Since Sir David Cox’s pioneering work in 1972, the proportional hazards model has become the most important model in survival analysis. The success of the Cox model stimulated further studies in semiparametric and nonparametric theories, counting process models, study designs in epidemiology, and the development of many other regression models that could offer more flexible or more suitable approaches in data analysis. Flexible semiparametric regression models are increasingly being used to relate lifetime distributions to time-dependent explanatory variables. Throughout the book, various recent statistical models are developed in close connection with specific data from experimental studies in clinical trials or from observational studies.
| Author | : Wei Pan |
| Publisher | : |
| Total Pages | : 224 |
| Release | : 1997 |
| Genre | : |
| ISBN | : |
| Author | : Mara Tableman |
| Publisher | : CRC Press |
| Total Pages | : 277 |
| Release | : 2003-07-28 |
| Genre | : Mathematics |
| ISBN | : 0203501411 |
Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
| Author | : John P. Klein |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 474 |
| Release | : 1992-02-29 |
| Genre | : Mathematics |
| ISBN | : 9780792316343 |
Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.
| Author | : D.R. Cox |
| Publisher | : CRC Press |
| Total Pages | : 216 |
| Release | : 1984-06-01 |
| Genre | : Mathematics |
| ISBN | : 9780412244902 |
This monograph contains many ideas on the analysis of survival data to present a comprehensive account of the field. The value of survival analysis is not confined to medical statistics, where the benefit of the analysis of data on such factors as life expectancy and duration of periods of freedom from symptoms of a disease as related to a treatment applied individual histories and so on, is obvious. The techniques also find important applications in industrial life testing and a range of subjects from physics to econometrics. In the eleven chapters of the book the methods and applications of are discussed and illustrated by examples.
| Author | : Yanjun Liu |
| Publisher | : |
| Total Pages | : 30 |
| Release | : 2017 |
| Genre | : Statistics |
| ISBN | : 9780355628647 |
To analyze spatially correlated time-to-event data, PH model is the current most commonly used semiparametric survival model. This paper extends Zhou and Hanson[(2017), 'A unified framework for fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data', Journal of the American Statistics Association, in press]'s framework which incorporates PH, AFT, and PO models by adding another competing model, the AH model, and updating their R package spBayesSurv. By using the survregbayes function in this R package, users can easily t and compare PH, PO, AFT, and AH model. Having another easy-to-fit model available for comparison is meaningful, especially when we have data that we suspect a lag period existing before a treatment becomes fully effective.
| Author | : Jialiang Li |
| Publisher | : CRC Press |
| Total Pages | : 385 |
| Release | : 2013-06-04 |
| Genre | : Mathematics |
| ISBN | : 143989311X |
Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples. Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.
| Author | : Catherine Legrand |
| Publisher | : CRC Press |
| Total Pages | : 361 |
| Release | : 2021-03-22 |
| Genre | : Mathematics |
| ISBN | : 0429622554 |
Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.
