Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Non And Semi Parametric Survival Analysis With Left Truncated And Interval Censored Data PDF full book. Access full book title Non And Semi Parametric Survival Analysis With Left Truncated And Interval Censored Data.
| Author | : Wei Pan |
| Publisher | : |
| Total Pages | : 224 |
| Release | : 1997 |
| Genre | : |
| ISBN | : |
| Author | : Kris Bogaerts |
| Publisher | : CRC Press |
| Total Pages | : 617 |
| Release | : 2017-11-20 |
| Genre | : Mathematics |
| ISBN | : 1420077481 |
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 | : John P. Klein |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 508 |
| Release | : 2013-06-29 |
| Genre | : Medical |
| ISBN | : 1475727283 |
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 | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2004 |
| Genre | : |
| ISBN | : |
(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 | : Jianguo Sun |
| Publisher | : Springer |
| Total Pages | : 310 |
| Release | : 2007-05-26 |
| Genre | : Mathematics |
| ISBN | : 0387371192 |
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 | : Jun Park |
| Publisher | : |
| Total Pages | : 262 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
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.
| Author | : Mara Tableman |
| Publisher | : CRC Press |
| Total Pages | : 277 |
| Release | : 2003-07-28 |
| Genre | : Mathematics |
| ISBN | : 0203501411 |
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 | : Ding-Geng (Din) Chen |
| Publisher | : CRC Press |
| Total Pages | : 426 |
| Release | : 2012-07-19 |
| Genre | : Mathematics |
| ISBN | : 1466504285 |
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 | : John P. Klein |
| Publisher | : CRC Press |
| Total Pages | : 635 |
| Release | : 2016-04-19 |
| Genre | : Mathematics |
| ISBN | : 146655567X |
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
| Author | : Yongxian Long |
| Publisher | : |
| Total Pages | : |
| Release | : 2017-01-26 |
| Genre | : |
| ISBN | : 9781361239445 |
This dissertation, "Semiparametric Analysis of Interval Censored Survival Data" by Yongxian, Long, 龙泳先, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4554115 Subjects: Survival analysis (Biometry) Medicine - Research - Statistical methods Parameter estimation
