Shared Frailty Survival Analysis Using Semiparametric Bayesian Method PDF Download
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Author | : Prof Shaban |
Publisher | : |
Total Pages | : 0 |
Release | : 2015 |
Genre | : |
ISBN | : |
Download Shared Frailty Survival Analysis Using Semiparametric Bayesian Method Book in PDF, ePub and Kindle
In survival data analysis, the proportional hazard model was introduced by Cox (1972) in order to estimate the effects of different covariates influencing the time-to-event data. The proportional hazard model has been used extensively in biomedicine, reliability engineering and, recently, interest in its application in different areas of knowledge has increased. However, proportional hazard model makes a number of assumptions, which may be violated. The object of this article is to present a Bayesian analysis for survival models with frailty under additive framework for the hazard function in contrast to proportional hazard model. Frailty models in survival analysis deal with the unobserved heterogeneity among subjects. Gibbs sampling technique is used to assess the posterior quantities of interest. An illustrative analysis within the context of survival time data is given.
Author | : Luc Duchateau |
Publisher | : Springer Science & Business Media |
Total Pages | : 329 |
Release | : 2007-10-23 |
Genre | : Mathematics |
ISBN | : 038772835X |
Download The Frailty Model Book in PDF, ePub and Kindle
Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.
Author | : David D. Hanagal |
Publisher | : Springer Nature |
Total Pages | : 307 |
Release | : 2019-11-16 |
Genre | : Medical |
ISBN | : 9811511810 |
Download Modeling Survival Data Using Frailty Models Book in PDF, ePub and Kindle
This book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.
Author | : Andreas Wienke |
Publisher | : CRC Press |
Total Pages | : 324 |
Release | : 2010-07-26 |
Genre | : Mathematics |
ISBN | : 9781420073911 |
Download Frailty Models in Survival Analysis Book in PDF, ePub and Kindle
The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.
Author | : John P. Klein |
Publisher | : Springer Science & Business Media |
Total Pages | : 446 |
Release | : 2013-03-09 |
Genre | : Mathematics |
ISBN | : 9401579830 |
Download Survival Analysis: State of the Art Book in PDF, ePub and Kindle
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 | : Il Do Ha |
Publisher | : Springer |
Total Pages | : 288 |
Release | : 2018-01-02 |
Genre | : Mathematics |
ISBN | : 9811065578 |
Download Statistical Modelling of Survival Data with Random Effects Book in PDF, ePub and Kindle
This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.
Author | : Philip Hougaard |
Publisher | : Springer Science & Business Media |
Total Pages | : 559 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461213045 |
Download Analysis of Multivariate Survival Data Book in PDF, ePub and Kindle
Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.
Author | : John P. Klein |
Publisher | : CRC Press |
Total Pages | : 635 |
Release | : 2016-04-19 |
Genre | : Mathematics |
ISBN | : 146655567X |
Download Handbook of Survival Analysis Book in PDF, ePub and Kindle
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
Author | : Joseph G. Ibrahim |
Publisher | : Springer Science & Business Media |
Total Pages | : 494 |
Release | : 2013-03-09 |
Genre | : Medical |
ISBN | : 1475734476 |
Download Bayesian Survival Analysis Book in PDF, ePub and Kindle
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.
Author | : Jianchang Lin |
Publisher | : |
Total Pages | : |
Release | : 2012 |
Genre | : Statistics |
ISBN | : |
Download Semiparametric Survival Analysis Using Models with Log-Linear Median Book in PDF, ePub and Kindle
ABSTRACT: First, we present two novel semiparametric survival models with log-linear median regression functions for right censored survival data. These models are useful alternatives to the popular Cox (1972) model and linear transformation models (Cheng et al., 1995). Compared to existing semiparametric models, our models have many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our modeling techniques facilitate the ease of prior elicitation and computation for both parametric and semiparametric Bayesian analysis of survival data. We illustrate the advantages of our modeling, as well as model diagnostics, via reanalysis of a small-cell lung cancer study. Results of our simulation study provide further guidance regarding appropriate modelling in practice. Our second goal is to develop the methods of analysis and associated theoretical properties for interval censored and current status survival data. These new regression models use log-linear regression function for the median. We present frequentist and Bayesian procedures for estimation of the regression parameters. Our model is a useful and practical alternative to the popular semiparametric models which focus on modeling the hazard function. We illustrate the advantages and properties of our proposed methods via reanalyzing a breast cancer study. Our other aim is to develop a model which is able to account for the heteroscedasticity of response, together with robust parameter estimation and outlier detection using sparsity penalization. Some preliminary simulation studies have been conducted to compare the performance of proposed model and existing median lasso regression model. Considering the estimation bias, mean squared error and other identication benchmark measures, our proposed model performs better than the competing frequentist estimator.