Tree-structured Modeling for Interval-censored Survival Data
Author: Yanming Yin
Publisher:
Published: 2002
Total Pages: 190
ISBN-13:
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Multi-State Survival Models for Interval-Censored Data
Author: Ardo van den Hout
Publisher: CRC Press
Published: 2016-11-25
Total Pages: 257
ISBN-13: 1466568410
DOWNLOAD EBOOKMulti-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.
Survival Analysis with Interval-Censored Data
Author: Kris Bogaerts
Publisher: CRC Press
Published: 2017-11-20
Total Pages: 617
ISBN-13: 1420077481
DOWNLOAD EBOOKSurvival 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.
Emerging Topics in Modeling Interval-Censored Survival Data
Author: Jianguo Sun
Publisher: Springer Nature
Published: 2022-11-29
Total Pages: 322
ISBN-13: 3031123662
DOWNLOAD EBOOKThis 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.
Statistical Properties of Tree-structured Models for Survival Data
Author: Weimin Gao
Publisher:
Published: 2003
Total Pages: 78
ISBN-13:
DOWNLOAD EBOOKValidation of Tree-structured Prediction for Censored Survival Data
Author: Abdissa Negassa
Publisher:
Published: 1996
Total Pages: 634
ISBN-13:
DOWNLOAD EBOOK"Objectives. (i) to develop a computationally efficient algorithm of tree-growing for censored survival data, (ii) to assess the performance of two validation schemes, and (iii) to evaluate the performance of computationally inexpensive model selection criteria in relation to cross-validation." --
Modelling Survival Data in Medical Research, Second Edition
Author: David Collett
Publisher: CRC Press
Published: 2003-03-28
Total Pages: 413
ISBN-13: 1584883251
DOWNLOAD EBOOKCritically 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.
Handbook of Survival Analysis
Author: John P. Klein
Publisher: CRC Press
Published: 2016-04-19
Total Pages: 635
ISBN-13: 146655567X
DOWNLOAD EBOOKHandbook 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
Unified Methods for Censored Longitudinal Data and Causality
Author: Mark J. van der Laan
Publisher: Springer Science & Business Media
Published: 2012-11-12
Total Pages: 412
ISBN-13: 0387217002
DOWNLOAD EBOOKA fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. students. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.
Imputation Based on Local Likelihood Density Estimation for Interval Censored Survival Data with Application to Tree Mortality in British Columbia
Author: Soyean Kim
Publisher:
Published: 2009
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKCensored data arise in many situations including forestry and medical studies, and may take several forms. In this project, we consider imputation methods for estimating lifetimes when interval censored data are available. We investigate an imputation method based on local likelihood density estimation, where kernel smoothing is used to estimate the underlying distribution of lifetimes in order to calculate the conditional expectation of the observed lifetime. We contrast this with a simple midpoint estimator, where the imputed lifetime is the midpoint of the interval censored data. We compare the two imputation methods in the context of an analysis of tree mortality in British Columbia. The main goal of the project is to describe the relationships between tree lifetimes and important covariates such as thinning levels and species of trees while observing how the use of different imputation methods can affect the derived relationships.
