Modelling Multivariate Interval Censored And Left Truncated Survival Data Using Proportional Hazards Model PDF Download
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| Author | : |
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| Release | : 2004 |
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Download Modelling Multivariate Interval-censored and Left-truncated Survival Data Using Proportional Hazards Model Book in PDF, ePub and Kindle
(Uncorrected OCR) Abstract of the thesis entitled MODELLING MULTIVARIATE INTERVAL-CENSORED AND LEFT-TRUNCATED SURVIVAL DATA USING PROPORTIONAL HAZARDS MODEL submitted by CHEUNG Tak Lun Alan for the degree of Master of Philosophy at The University of Hong Kong in December 2003 One of the main objectives in survival analysis is to investigate the effects of some potential explanatory variables on the survival times. One popular model used in such analysis is the celebrated Cox semiparametric proportional hazards model. Cox (1975) considered the partial likelihood, which only uses the rank of the uncensored survival times, to estimate regression parameter. However, the rank of the failure times is not available in the presence of interval censoring because it is too expensive or even impossible to monitor the experimental subjects continuously in most controlled clinical trials. To model interval-censored data with covariates, a simple multiple imputation approach is proposed to estimate the regression parameter of the Cox model. The basic idea is to iterate between the following two steps. With an additional Weibull assumption on the baseline hazard function, we first impute an exact failure time to each finite interval-censored time using the approximate conditional posterior distribution. Secondly, the standard Cox partial likelihood is applied to the imputed data and the estimate of the regression parameter is updated. The two steps are performed iteratively until convergence is achieved. Robust variance estimator for the regression parameter is also suggested to address the misspecification of the baseline hazard function. Although a parametric Weibull basline hazard function is specified, simulation studies show that the proposed method performs extremely well even when the baseline hazard function is piecewise constant. Applications to real life examples are provided. Practically, we cannot assume that the survival times of distinct individuals are independent t.
| Author | : Tak-lun Cheung (M.Phil., Alan) |
| Publisher | : |
| Total Pages | : 220 |
| Release | : 2003 |
| Genre | : Failure time data analysis |
| ISBN | : |
Download Modelling Multivariate Interval-censored and Left-truncated Survival Data Using Proportional Hazards Model Book in PDF, ePub and Kindle
| Author | : Mara Tableman |
| Publisher | : CRC Press |
| Total Pages | : 277 |
| Release | : 2003-07-28 |
| Genre | : Mathematics |
| ISBN | : 0203501411 |
Download Survival Analysis Using S Book in PDF, ePub and Kindle
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.
| 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 | : Philip Hougaard |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 559 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461213045 |
Download Analysis of Multivariate Survival Data Book in PDF, ePub and Kindle
Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.
| 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 | : John P. Klein |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 508 |
| Release | : 2013-06-29 |
| Genre | : Medical |
| ISBN | : 1475727283 |
Download Survival Analysis Book in PDF, ePub and Kindle
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.
| Author | : David Collett |
| Publisher | : CRC Press |
| Total Pages | : 538 |
| Release | : 2015-05-04 |
| Genre | : Mathematics |
| ISBN | : 1498731694 |
Download Modelling Survival Data in Medical Research Book in PDF, ePub and Kindle
Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range of examples from biomedical research.Well known for its nontechnical style, this third edition contains new chapters on frailty models and their applications, competing risks, non-proportional hazards, and dependent censo
| Author | : Wei Pan |
| Publisher | : |
| Total Pages | : 224 |
| Release | : 1997 |
| Genre | : |
| ISBN | : |
Download Non- and Semi-parametric Survival Analysis with Left Truncated and Interval Censored Data Book in PDF, ePub and Kindle
| 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.
