Emerging Topics In Modeling Interval Censored Survival Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Emerging Topics In Modeling Interval Censored Survival Data PDF full book. Access full book title Emerging Topics In Modeling Interval Censored Survival Data.
Author | : Jianguo Sun |
Publisher | : Springer Nature |
Total Pages | : 322 |
Release | : 2022-11-29 |
Genre | : Mathematics |
ISBN | : 3031123662 |
Download Emerging Topics in Modeling Interval-Censored Survival Data Book in PDF, ePub and Kindle
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 | : Kris Bogaerts |
Publisher | : CRC Press |
Total Pages | : 617 |
Release | : 2017-11-20 |
Genre | : Mathematics |
ISBN | : 1420077481 |
Download Survival Analysis with Interval-Censored Data Book in PDF, ePub and Kindle
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.
Author | : Ardo van den Hout |
Publisher | : CRC Press |
Total Pages | : 257 |
Release | : 2016-11-25 |
Genre | : Mathematics |
ISBN | : 1466568410 |
Download Multi-State Survival Models for Interval-Censored Data Book in PDF, ePub and Kindle
Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.
Author | : Jianguo Sun |
Publisher | : Springer |
Total Pages | : 304 |
Release | : 2007-05-26 |
Genre | : Mathematics |
ISBN | : 0387371192 |
Download The Statistical Analysis of Interval-censored Failure Time Data Book in PDF, ePub and Kindle
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.
Author | : David Collett |
Publisher | : CRC Press |
Total Pages | : 557 |
Release | : 2023-05-31 |
Genre | : Medical |
ISBN | : 1000863107 |
Download Modelling Survival Data in Medical Research Book in PDF, ePub and Kindle
Hugely popular textbook on survival analysis for graduate students of statistics and biostatistics, mainly due to its accessibility and breadth of examples. This is a standard course on graduate programs in biostatistics and statistics, and this is one of the most popular textbooks. Updated with modern methods covering Bayesian survival analysis, joint models, and more.
Author | : Tyler Cook |
Publisher | : |
Total Pages | : 77 |
Release | : 2015 |
Genre | : |
ISBN | : |
Download Model Evaluation and Variable Selection for Interval-censored Data Book in PDF, ePub and Kindle
Survival analysis is a popular area of statistics dealing with time-to-event data. This type of data can be seen in many disciplines, but it is perhaps most commonly encountered in medical studies. Doctors, for example, might be testing different treatments developed to prolong the lifetimes of cancer patients. Unfortunately, in practical problems such as clinical trials, there is often incomplete data thanks to patients dropping out of the study. This results in censoring, which is a special characteristic of survival data. There are many different types of censoring. This dissertation focuses on the analysis of interval-censored data, where the failure time is only known to belong to some interval of observation times. One problem that researchers face when analyzing survival data is how to handle the censoring distribution. It is often assumed that the observation process generating the censoring is independent of the event time of interest. Consequently, the observation process can effectively be ignored. However, this assumption is clearly not always realistic. Unfortunately, one cannot generally test for independent censoring without additional assumptions or information. Therefore, the researcher is faced with a choice between using methods designed for informative or noninformative censoring. Chapters 2 and 3 of this dissertation investigate the effectiveness of different methods developed for the analysis of informative case I and case II interval censored data under both types of censoring. Extensive simulation studies indicate that the methods produce unbiased results in the presence of both informative and noninformative censoring. The efficiency of the informative censoring methods is then compared with approaches created to handle noninformative censoring. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the dependence of the censoring distribution. Another important problem seen in survival analysis is determining the set of predictors that are significantly related with the failure time being studied. Variable selection has received substantial attention both in classical linear models as well as survival analysis. This is largely thanks to recent technological advances making it easier for researchers in biology to collect huge amounts of genetic data. For example, a researcher with access to gene expression levels for hundreds of genes is interested in identifying which of those genes can predict tumor development time in cancer patients. One must sift through the large number of genes in order to find the small set of significant genes that influence tumor growth. Several methods using penalized likelihood procedures have been proposed to perform parameter estimation and variable selection simultaneously. A number of these techniques have also been extended to the case of right-censored survival data, but little has been done in the context of interval-censoring. In chapter 4, we propose an imputation approach for variable selection of interval-censored data that utilizes these penalized likelihood procedures. This method uses imputation to create a new dataset of imputed exact failure times and right-censored observations. Variable selection can then be performed on the imputed dataset using any of the popular variable selection techniques created for right-censored data. Comprehensive simulation studies illustrate the effectiveness of this new approach. Also, this method is attractive due to how easy it is to implement, since it can take advantage of existing software for variable selection of right-censored data.
Author | : Ding-Geng (Din) Chen |
Publisher | : CRC Press |
Total Pages | : 426 |
Release | : 2012-07-19 |
Genre | : Mathematics |
ISBN | : 1466504285 |
Download Interval-Censored Time-to-Event Data Book in PDF, ePub and Kindle
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
Author | : Ramon Oller Piqué |
Publisher | : |
Total Pages | : 160 |
Release | : 2006 |
Genre | : |
ISBN | : 9788469019306 |
Download Survival analysis issues with interval-censored data[ Book in PDF, ePub and Kindle
Author | : David Collett |
Publisher | : CRC Press |
Total Pages | : 413 |
Release | : 2003-03-28 |
Genre | : Mathematics |
ISBN | : 1584883251 |
Download Modelling Survival Data in Medical Research, Second Edition Book in PDF, ePub and Kindle
Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.
Author | : Yanming Yin |
Publisher | : |
Total Pages | : 190 |
Release | : 2002 |
Genre | : |
ISBN | : |
Download Tree-structured Modeling for Interval-censored Survival Data Book in PDF, ePub and Kindle