Download or read book Semiparametric Analysis of Interval Censored Survival Data written by Yongxian Long and published by . This book was released on 2010 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Analysis of Interval Censored Survival Data written by Yongxian Long and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 435 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. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.

Download or read book The Statistical Analysis of Interval censored Failure Time Data written by Jianguo Sun and published by Springer. This book was released on 2007-05-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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 Emerging Topics in Modeling Interval Censored Survival Data written by Jianguo Sun and published by Springer Nature. This book was released on 2022-11-29 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Semi parametric Regression Analysis of Interval censored Failure Time Data written by Ling Ma and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: By interval-censored data, we mean that the failure time of interest is known only to lie within an interval instead of being observed exactly. Many clinical trials and longitudinal studies may generate interval-censored data. One common example occurs in medical or health studies that entail periodic follow-ups. An important special case of interval-censored data is the so called current status data when each subject is observed only once for the status of the occurrence of the event of interest. That is, instead of observing the survival endpoint directly, we only know the observation time and whether or not the event of interest has occurred at that time. Such data may occur in many fields, for example, cross-sectional studies and tumorigenicity experiments. Sometimes we also refer current status data to as case I interval-censored data and the general case as case II interval-censored data. In the following, for simplicity, we will refer current status data and interval-censored data to case I and case II interval-censored data, respectively. The statistical analysis of both case I and case II interval-censored failure time data has recently attracted a great deal of attention and especially, many procedures have been proposed for their regression analysis under various models. However, due to the strict restrictions of existing regression analysis procedures and practical demands, new methodologies for regression analysis need to be developed. For regression analysis of interval-censored data, many approaches have been proposed and for most of them, the inference is carried out based on the asymptotic normality. It's well known that the symmetric property implied by the normal distribution may not be appropriate sometimes and could underestimate the variance of estimated parameters. In the first part of this dissertation, we adopt the linear transformation models for regression analysis of interval-censored data and propose an empirical likelihood-based procedure to address the underestimating problem from using symmetric property implied by the normal distribution of the parameter estimates. Simulation and analysis of a real data set are conducted to assess the performance of the procedure. The second part of this dissertation discusses regression analysis of current status data under additive hazards models. In this part, we focus on the situation when some covariates could be missing or cannot be measured exactly due to various reasons. Furthermore, for missing covariates, there may exist some related information such as auxiliary covariates (Zhou and Pepe, 1995). We propose an estimated partial likelihood approach for estimation of regression parameters that make use of the available auxiliary information. To assess the finite sample performance of the proposed method, an extensive simulation study is conducted and indicates that the method works well in practical situations. Several semi-parametric and non-parametric methods have been proposed for the analysis of current status data. However, most of these methods deal only with the situation where observation time is independent of the underlying survival time completely or given covariates. The third part of this dissertation discusses regression analysis of current status data when the observation time may be related to survival time. The correlation between observation time and survival time and the covariate effects are described by a copula model and the proportional hazards model, respectively. For estimation, a sieve maximum likelihood procedure with the use of monotone I-spline functions is proposed and the proposed method is examined through a simulation study and illustrated with a real data set. In the fourth part of this dissertation, we discuss the regression analysis of interval- censored data where the censoring mechanism could be related to the failure time. We consider a situation where the failure time depend on the censoring mechanism only through the length of the observed interval. The copula model and monotone I-splines are used and the asymptotic properties of the resulting estimates are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An extensive simulation study and an illustrative example is provided. Finally, we will talk about the directions for future research. One topic related the fourth part of this dissertation for future research could be to allow the failure time to depend on both the lower and upper bounds of the observation interval. Another possible future research topic could be to consider a cure rate model for interval-censored data with informative censoring.

Download or read book Non and Semi parametric Survival Analysis with Left Truncated and Interval Censored Data written by Wei Pan and published by . This book was released on 1997 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric and Semiparametric Methods for Interval censored Failure Time Data written by Chao Zhu and published by . This book was released on 2006 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-censored failure time data commonly arise in follow-up studies such as clinical trials and epidemiology studies. For their analysis, what interests researcher most includes comparisons of survival functions for different groups and regression analysis. This dissertation, which consists of three parts, consider these problems on two types of interval-censored data by using nonparametric and semiparametric methods.

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 John P. Klein and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, 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, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology.

Download or read book Semiparametric Regression Under Left truncated and Interval censored Competing Risks Data and Missing Cause of Failure written by Jun Park and published by . This book was released on 2020 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Observational studies and clinical trials with time-to-event data frequently involve multiple event types, known as competing risks. The cumulative incidence function (CIF) is a particularly useful parameter as it explicitly quantifies clinical prognosis. Common issues in competing risks data analysis on the CIF include interval censoring, missing event types, and left truncation. Interval censoring occurs when the event time is not observed but is only known to lie between two observation times, such as clinic visits. Left truncation, also known as delayed entry, is the phenomenon where certain participants enter the study after the onset of disease under study. These individuals with an event prior to their potential study entry time are not included in the analysis and this can induce selection bias. In order to address unmet needs in appropriate methods and software for competing risks data analysis, this thesis focuses the following development of application and methods. First, we develop a convenient and exible tool, the R package intccr, that performs semiparametric regression analysis on the CIF for interval-censored competing risks data. Second, we adopt the augmented inverse probability weighting method to deal with both interval censoring and missing event types. We show that the resulting estimates are consistent and double robust. We illustrate this method using data from the East-African International Epidemiology Databases to Evaluate AIDS (IeDEA EA) where a significant portion of the event types is missing. Last, we develop an estimation method for semiparametric analysis on the CIF for competing risks data subject to both interval censoring and left truncation. This method is applied to the Indianapolis-Ibadan Dementia Project to identify prognostic factors of dementia in elder adults. Overall, the methods developed here are incorporated in the R package intccr.

Download or read book Nonparametric and Semiparametric Censored Survival Analysis of Correlated Times to Event and Their Sequelae written by Adin-Cristian Andrei and published by . This book was released on 2005 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Regression Analysis of Interval censored Failure Time Data with Non Proportional Hazards Models written by Han Zhang (Graduate of University of Missouri) and published by . This book was released on 2018 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-censored failure time data arises when the failure time of interest is known only to lie within an interval or window instead of being observed exactly. Many clinical trials and longitudinal studies may generate interval-censored data. One common area that often produces such data is medical or health studies with periodic follow-ups, in which the medical condition of interest such as the onset of a disease is only known to occur between two adjacent examination times. An important special case of interval-censored data is the so-called current status data when each study subject is observed only once for the status of the event of interest. That is, instead of observing the survival endpoint directly, we will only know the observation time and whether or not the event of interest has occurred by that time. Such data may occur in many fields as cross-sectional studies and tumorigenicity experiments. Sometimes we also refer current status data as case I interval-censored data and the general case as case II interval-censored data. Recently the semi-parametric statistical analysis of both case I and case II intervalcensored failure time data has attracted a great deal of attention. Many procedures have been proposed for their regression analysis under various models. We will describe the structure of interval-censored data in Chapter 1 and provides two specific examples. Also some special situations like informative censoring and failure time data with missing covariates are discussed. Besides, a brief review of the literature on some important topics, including nonparametric estimation and regression analysis are performed. However, there are still a number of problems that remain unsolved or lack approaches that are simpler, more efficient and could apply to more general situations compared to the existing ones. For regression analysis of interval-censored data, many approaches have been proposed and more specifically most of them are developed for the widely used proportional hazards model. The research in this dissertation focuses on the statistical analysis on non-proportional hazards models. In Chapter 2 we will discuss the regression analysis of interval-censored failure time data with possibly crossing hazards. For the problem of crossing hazards, people assume that the hazard functions with two samples considered may cross each other where most of the existing approaches cannot deal with such situation. Many authors has provided some efficient methods on right-censored failure time data, but little articles could be found on interval-censored data. By applying the short-term and long-term hazard ratio model, we develop a spline-based maximum likelihood estimation procedure to deal with this specific situation. In the method, a splined-based sieve estimation are used to approximate the underlying unknown function. The proposed estimators are shown to be strongly consistent and the asymptotic normality of the estimators of regression parameters are also shown to be true. In addition, we also provided a Cramer-Raw type of criterion to do the model validation. Simulation study was conducted for the assessment of the finite sample properties of the presented procedure and suggests that the method seems to work well for practical situations. Also an illustrative example using a data set from a tumor study is provided. As we discussed in Chapter 1, several semi-parametric and non-parametric methods have been proposed for the analysis of current status data. However, most of them only deal with the situation where observation time is independent of the underlying survival time. In Chapter 3, we consider regression analysis of current status data with informative observation times in additive hazards model. In many studies, the observation time may be correlated to the underlying failure time of interest, which is often referred to as informative censoring. Several authors have discussed the problem and in particular, an estimating equation-based approach for fitting current status data to additive hazards model has been proposed previously when informative censoring occurs. However, it is well known that such procedure may not be efficient and to address this, we propose a sieve maximum likelihood procedure. In particular, an EM algorithm is developed and the resulting estimators of regression parameters are shown to be consistent and asymptotically normal. An extensive simulation study was conducted for the assessment of the finite sample properties of the presented procedure and suggests that it seems to work well for practical situations. An application to a tumorigenicity experiment is also provided. In Chapter 4, we considered another special case under the additive hazards model, case II interval-censored data with possibly missing covariates. In many areas like demographical, epidemiological, medical and sociological studies, a number of nonparametric or semi-parametric methods have been developed for interval-censored data when the covariates are complete. However, it is well-known that in reality some covariates may suffer missingness due to various reasons, data with missing covariates could be very common in these areas. In the case of missing covariates, a naive method is clearly the complete-case analysis, which deletes the cases or subjects with missing covariates. However, it's apparent that such analysis could result in loss of efficiency and furthermore may lead to biased estimation. To address this, we propose the inverse probability weighted method and reweighting approach to estimate the regression parameters under the additive hazards model when some of the covariates are missing at random. The resulting estimators of regression parameters are shown to be consistent and asymptotically normal. Several numerical results suggest that the proposed method works well in practical situations. Also an application to a health survey is provided. Several directions for future research are discussed in Chapter 5.

Download or read book Modeling Discrete Time to Event Data written by Gerhard Tutz and published by Springer. This book was released on 2016-06-14 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

Download or read book Introducing Survival and Event History Analysis written by Melinda Mills and published by SAGE. This book was released on 2011-01-19 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, conversations with key scholars and resources for both students and instructors, this text allows researchers to quickly master advanced statistical techniques. It is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook. Also included are up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text.