Download or read book Survival Analysis with Complex Censoring Mechanisms with Applications in Population based Studies and Clinical Trials written by Megan Kay Diane Othus and published by . This book was released on 2009 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Population-based studies and clinical trials provide many interesting methodological problems that render important policy implications as well as better explainations of disease progression processes. This thesis is to answer three such questions. Trends in United States cancer survival motivated a statistical method for survival data that may be subject to dependent censoring in disease populations that may contain a portion of long-term cancer surviors. Prostate cancer trends motivated work on a survival model for populations that may have long-term survivors and that exhibit a change-point effect in important covariates or predictors. Finally, a clinical trial on childhood acute lymphoblastic leukemia motivated work on a survival model for clustered data that explicitly models the correlation of failure times but also allows for population-level interpretation of survival parameters.
Download or read book Survival Analysis written by John P. Klein and published by Springer Science & Business Media. This book was released on 2006-05-17 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.
Download or read book Survival Analysis in Medicine and Genetics written by Jialiang Li and published by CRC Press. This book was released on 2013-06-04 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real data sets throughout, this text introduces the latest methods for analyzing high-dimensional survival data. With an emphasis on the applications of survival analysis techniques in genetics, it presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. The book 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.
Download or read book Survival Analysis written by H. J. Vaman and published by Chapman & Hall/CRC. This book was released on 2022-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis"--
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.
Download or read book Survival Analysis of Complex Featured Data with Measurement Error written by Li-Pang Chen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis plays an important role in many fields, such as cancer research, clinical trials, epidemiological studies, actuarial science, and so on. A large body of methods on analyzing survival data have been developed. However, many important problems have still not been fully explored. In this thesis, we focus on the analysis of survival data with complex features. In Chapter 1, we review relevant topics including survival analysis, the measurement error model, the graphical model, and variable selection. Graphical models are useful in characterizing the dependence structure of variables. They have been commonly used for analysis of high-dimensional data, including genetic data and data with network structures. Many estimation procedures have been developed under various graphical models with a stringent assumption that the associated variables must be measured precisely. In applications, this assumption, however, is often unrealistic and mismeasurement in variables is usually presented in data. In Chapter 2, we investigate the high-dimensional graphical model with error-prone variables. We propose valid estimation procedures to account for measurement error effects. Theoretical results are established for the proposed methods and numerical studies are reported to assess the performance of our proposed methods. In Chapter 3, we consider survival analysis with network structures and measurement error in covariates. In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical proportional hazards measurement error models, and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods. In Chapter 4, we focus on sufficient dimension reduction for high-dimensional survival data with covariate measurement error. Sufficient dimension reduction (SDR) is an important tool in regression analysis which reduces the dimension of covariates without losing predictive information. Several methods have been proposed to handle data with either censoring in the response or measurement error in covariates. However, little research is available to deal with data having these two features simultaneously. Moreover, the analysis becomes more challenging when data contain ultrahigh-dimensional covariates. In Chapter 4, we examine this problem. We start with considering the cumulative distribution function in regular settings and propose a valid SDR method to incorporate the effects of both censored data and covariates measurement error. Next, we extend the proposed method to handle ultrahigh-dimensional data. Theoretical results of the proposed methods are established. Numerical studies are reported to assess the performance of the proposed methods. In Chapter 5, we slightly switch our attention to examine sampling issues concerning survival data. Specifically, we discuss survival analysis for left-truncated and right-censored data with covariate measurement error. Many methods have been developed for analyzing survival data which commonly involve right-censoring. These methods, however, are challenged by complex features pertinent to the data collection as well as the nature of data themselves. Typically, biased samples caused by left-truncation or length-biased sampling and measurement error are often accompanying with survival analysis. While such data frequently arise in practice, little work has been available in the literature. In Chapter 5, we study this important problem and explore valid inference methods for handling left-truncated and right-censored survival data with measurement error under the widely used Cox model. We exploit a flexible estimator for the survival model parameters which does not require specification of the baseline hazard function. To improve the efficiency, we further develop an augmented non-parametric maximum likelihood estimator. We establish asymptotic results for the proposed estimators and examine the efficiency and robustness issues of the proposed estimators. The proposed methods enjoy appealing features that the distributions of the covariates and of the truncation times are left unspecified. Numerical studies are reported to assess the performance of the proposed methods. In Chapter 6, we study outstanding issues on model selection and model averaging for survival data with measurement error. Model selection plays a critical role in statistical inference and a vast literature has been devoted to this topic. Despite extensive research attention on model selection, research gaps still remain. An important but unexplored problem concerns model selection for truncated and censored data with measurement error. Although analysis of left-truncated and right-censored (LTRC) data has received extensive interests in survival analysis, there has been no research on model selection for LTRC data, let alone LTRC data involving with measurement error. In Chapter 6, we take up this important problem and develop inferential procedures to handle model selection for LTRC data with measurement error in covariates. Our development employs the local model misspecification framework and emphasizes the use of the focus information criterion (FIC). We develop valid estimators using the model averaging scheme and establish theoretical results to justify the validity of our methods. Numerical studies are conducted to assess the performance of the proposed methods. Finally, Chapter 7 summarizes the thesis with discussions.
Download or read book Interval Censored Time to Event Data written by Ding-Geng (Din) Chen and published by CRC Press. This book was released on 2012-07-19 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.Divid
Download or read book Survival Analysis written by David Machin and published by John Wiley & Sons. This book was released on 2006-03-30 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Well received in its first edition, Survival Analysis: A Practical Approach is completely revised to provide an accessible and practical guide to survival analysis techniques in diverse environments. Illustrated with many authentic examples, the book introduces basic statistical concepts and methods to construct survival curves, later developing them to encompass more specialised and complex models. During the years since the first edition there have been several new topics that have come to the fore and many new applications. Parallel developments in computer software programmes, used to implement these methodologies, are relied upon throughout the text to bring it up to date.
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.
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
Download or read book Survival Analysis written by David G. Kleinbaum and published by Springer. This book was released on 2006-01-02 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: An excellent introduction for all those coming to the subject for the first time. New material has been added to the second edition and the original six chapters have been modified. The previous edition sold 9500 copies world wide since its release in 1996. 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.
Download or read book Survival Analysis written by David G. Kleinbaum and published by Springer. This book was released on 2011-10-15 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: An excellent introduction for all those coming to the subject for the first time. New material has been added to the second edition and the original six chapters have been modified. The previous edition sold 9500 copies world wide since its release in 1996. 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.
Download or read book Survival Analysis written by Rupert G. Miller and published by John Wiley & Sons. This book was released on 1981 with total page 304 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.
Download or read book Analysing Survival Data from Clinical Trials and Observational Studies written by Ettore Marubini and published by Wiley. This book was released on 1995-07-05 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed.
Download or read book Analysis of Survival Data with Dependent Censoring written by Takeshi Emura and published by . This book was released on 2018 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients' survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio- ) statisticians. To deepen readers' understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.
Download or read book Applied Survival Analysis Using R written by Dirk F. Moore and published by Springer. This book was released on 2016-05-11 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.
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.
