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Bayesian Lasso Survival Analysis
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Book Synopsis Bayesian LASSO Survival Analysis by : Justin P. Neely
Download or read book Bayesian LASSO Survival Analysis written by Justin P. Neely and published by . This book was released on 2019 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: his thesis examines the use of Bayesian LASSO regression for survival data to estimate the survival function and to select significant covariates simultaneously. We consider survival times of patients with adenocarcinoma lung cancer. The survival and genetic data are available in the Cancer Genome Atlas (TCGA) Research Network. As a pilot study, within chromosome 5, we apply Bayesian LASSO regression to explore genetic markers that may help to identify crucial genes to determine survival times of patients. Using Gibbs sampling we can obtain Markov Chain Monte Carlo samples for regression coefficients and model variance as well as LASSO penalty from their full conditional distribution. However,under the Cox Proportional Hazard model sampling from the full conditional distribution for the Bayesian LASSO regression coefficients is computationally difficult. Therefore, we use latent variables for survival likelihood and perform Bayesian inference. We compare the Bayesian LASSO with a common variable selection method and a Frequentist LASSO for the estimation of the survival function and identified critical covariates.
Book Synopsis Bayesian Survival Analysis by : Joseph G. Ibrahim
Download or read book Bayesian Survival Analysis written by Joseph G. Ibrahim and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Bayesian Survival Analysis by : Joseph G. Ibrahim
Download or read book Bayesian Survival Analysis written by Joseph G. Ibrahim and published by . This book was released on 2014-01-15 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Survival Analysis: State of the Art by : John P. Klein
Download or read book Survival Analysis: State of the Art written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Survival Analysis by : John P. Klein
Download or read book Handbook of Survival Analysis written by John P. Klein and published by CRC Press. This book was released on 2016-04-19 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Bayesian Variable Selection in Parametric and Semiparametric High Dimensional Survival Analysis by : Kyu Ha Lee
Download or read book Bayesian Variable Selection in Parametric and Semiparametric High Dimensional Survival Analysis written by Kyu Ha Lee and published by . This book was released on 2011 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we propose several Bayesian variable selection schemes forBayesian parametric and semiparametric survival models for right-censored survivaldata. In the rst chapter we introduce a special shrinkage prior on the coe cients corresponding to the predictor variables. The shrinkage prior is obtained through a scale mixture representation of Normal and Gamma distributions. The likelihood functionis constructed based on the Cox proportional hazards model framework, where the cumulative baseline hazard function is modeled a priori by a gamma process. In the second chapter we extend the idea of the shrinkage prior such that it can incorporate the existing grouping structure among the covariates. Our selected priors are similar to the elastic-net, group lasso, and fused lasso penalty. The proposed models are highly useful when we want to take into consideration the grouping structure. In the third chapter we propose a Bayesian variable selection method for high dimensional survival analysis in the context of parametric accelerated failure time (AFT) model. To identify subsets of relevant covariates the regression coe cients are assumed to follow the conditional Laplace distribution as in the rst chapter. We used a data augmentation approach to impute the survival times of censored subjects.
Book Synopsis Survival Analysis by : John O'Quigley
Download or read book Survival Analysis written by John O'Quigley and published by Springer Nature. This book was released on 2021-04-27 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an extensive coverage of the methodology of survival analysis, ranging from introductory level material to deeper more advanced topics. The framework is that of proportional and non-proportional hazards models; a structure that is broad enough to enable the recovery of a large number of established results as well as to open the way to many new developments. The emphasis is on concepts and guiding principles, logical and graphical. Formal proofs of theorems, propositions and lemmas are gathered together at the end of each chapter separate from the main presentation. The intended audience includes academic statisticians, biostatisticians, epidemiologists and also researchers in these fields whose focus may be more on the applications than on the theory. The text could provide the basis for a two semester course on survival analysis and, with this goal in mind, each chapter includes a section with a range of exercises as a teaching aid for instructors.
Book Synopsis Survival Analysis with Interval-Censored Data by : Kris Bogaerts
Download or read book Survival Analysis with Interval-Censored Data written by Kris Bogaerts and published by CRC Press. This book was released on 2017-11-20 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years, interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition, the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features: -Provides an overview of frequentist as well as Bayesian methods. -Include a focus on practical aspects and applications. -Extensively illustrates the methods with examples using R, SAS, and BUGS. Full programs are available on a supplementary website. The authors: Kris Bogaerts is project manager at I-BioStat, KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University, Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society and editor of Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat, KU Leuven. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval-censored data, misclassification issues, and clinical trials. He is the founding chair of the Statistical Modelling Society, past-president of the International Society for Clinical Biostatistics, and fellow of ISI and ASA.
Book Synopsis Survival Analysis by : Rupert G. Miller, Jr.
Download or read book Survival Analysis written by Rupert G. Miller, Jr. and published by John Wiley & Sons. This book was released on 2011-01-25 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.
Book Synopsis Bayesian Survival Analysis by : Keith Rowland Abrams
Download or read book Bayesian Survival Analysis written by Keith Rowland Abrams and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Survival Analysis Using S by : Mara Tableman
Download or read book Survival Analysis Using S written by Mara Tableman and published by CRC Press. This book was released on 2003-07-28 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Modeling Survival Data Using Frailty Models by : David D. Hanagal
Download or read book Modeling Survival Data Using Frailty Models written by David D. Hanagal and published by Springer Nature. This book was released on 2019-11-16 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Statistical Modelling of Survival Data with Random Effects by : Il Do Ha
Download or read book Statistical Modelling of Survival Data with Random Effects written by Il Do Ha and published by Springer. This book was released on 2018-01-02 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Analysis of Survival Data by : D.R. Cox
Download or read book Analysis of Survival Data written by D.R. Cox and published by CRC Press. This book was released on 1984-06-01 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Bayesian Nonparametric Survival Analysis for Finite Populations by : Shou-ren Lai
Download or read book Bayesian Nonparametric Survival Analysis for Finite Populations written by Shou-ren Lai and published by . This book was released on 1994 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Survival Analysis written by Shenyang Guo and published by Oxford University Press. This book was released on 2010-01-25 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis is a class of statistical methods for studying the occurrence and timing of events. With clearly written summaries and plentiful examples, this pocket guide will put this important statistical tool in the hands of many more social work researchers than have been able to use it before.
Book Synopsis Bayesian Non-linear Methods for Survival Analysis and Structural Equation Models by : Zhenyu Wang (Statistician)
Download or read book Bayesian Non-linear Methods for Survival Analysis and Structural Equation Models written by Zhenyu Wang (Statistician) and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High dimensional data are more common nowadays, because the collection of such data becomes larger and more complex due to the technology advance of the computer science, biology, etc. The analysis of high dimensional data is different from traditional data analysis, and variable selection for high dimensional data becomes very challenging. Structural equation modeling (SEM) analyzes the relationship between manifest variables and latent variables. The structural equation focuses on analyzing the relationship between latent variables. New proposed methods of these topics are discussed in the dissertation. In the first chapter, we review the basic concept of survival analysis, SEM, and current method of variable selection in those two scenarios. We also introduce the available software package for current methods and relevant data set. In the second chapter, we develop a Bayesian kernel machine model with incorporating existing information on pathways and gene networks in the analysis of DNA microarray data. Each pathway is modeled nonparametrically using reproducing kernel Hilbert space. The pathways and the genes are selected via assigning mixture priors on the pathway indicator variable and the gene indicator variable. This approach helped us in flexible modeling of the pathway effects, which can capture both linear and non-linear effect. Moreover, the model can also pinpoint the important pathways and the important active genes within each pathway. We have also developed an efficient Markov Chain Monte Carlo (MCMC) algorithm to fit our model. We used simulations and a real data analysis, [van 't Veer et al., 2002] breast cancer microarray data, to illustrate the proposed method. In the third chapter, we extend the idea of semiparametric structural equation model where the nonlinear functional relationships are approximated using basis expansions [Guo et al., 2012]. Many basis expansion methods, including cubic splines, are known to induce correlations. In this chapter we compare standard Lasso, Fused Lasso and Elastic Net to account for correlations in both the covariate and basis expansions. To illustrate the usefulness of the proposed methods, a simulation study and a real data study have been performed. The semiparametric structural equation models based on Bayesian fused Lasso and Bayesian elastic-net outperform the Bayesian Lasso model. In the fourth chapter, we apply Bayesian Graph Laplacian Model, developed by [Liu et al., 2014] and generalized the graph Laplacian allowing both positively and negatively correlated variable, to analyze gene expression data from Michigan prostate cancer study [Dhanasekaran et al., 2001]. We find out the underlie gene network and interaction related to prostate cancer and discuss the possible extensions for Bayesian Graph Laplacian Model, including analyzing multiple pathways simultaneously and pathways selection, right censored data as response variable and binomial or multinomial data as response variable.
