Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Hazard Rate Estimation Based On Censored Data And Measurement Error
Download Hazard Rate Estimation Based On Censored Data And Measurement Error full books in PDF, epub, and Kindle. Read online Hazard Rate Estimation Based On Censored Data And Measurement Error ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Hazard Rate Estimation Based on Censored Data and Measurement Error by : Will Chen
Download or read book Hazard Rate Estimation Based on Censored Data and Measurement Error written by Will Chen and published by . This book was released on 2022 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated from lung cancer study data, we consider a model is an observable variable and Z is a hidden variable contaminated in X = Z + E, where X X with a measurement error E. Such a model can also apply to studies in microfluorimetry, electrophoresis, biostatistics, and some other fields, where the measurements Z cannot be observed directly. The objective of this project is to estimate the hazard rate of the unobservable survival time Z in a lung cancer study. Assuming the additive measurement error E has a known distribution, we combine deconvolution kernel density estimation and inverse-probability of-censoring weighting methods to formulate a nonparametric hazard rate estimator based on random right-censored observations of X, when the distribution of X is unknown. Simulation studies show that the estimator performs well when sample sizes are relatively large.
Book Synopsis Hazard Rate Estimation for Censored Data Via Strong Representation of the Kaplan-Meier Estimator by : S. H. Lo
Download or read book Hazard Rate Estimation for Censored Data Via Strong Representation of the Kaplan-Meier Estimator written by S. H. Lo and published by . This book was released on 1985 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This document studies the estimation of a hazard rate function based on censored data by the kernel smoothing method. Our technique is facilitated by a recent result of Lo and Singh (1984) which establishes a strong uniform approximation of the Kaplan-Meier estimator by an average of independent random variables. Pointwise and uniform strong consistency are derived, as well as the mean squared error expression and asymptotic normality, which is obtained using a more traditional method, as compared with the Hajek projection employed by Tanner and Wong (1983). (Author).
Book Synopsis Nonparametric Hazard Rate Estimation with Left Truncated and Right Censored Data by : Jufen Chu
Download or read book Nonparametric Hazard Rate Estimation with Left Truncated and Right Censored Data written by Jufen Chu and published by . This book was released on 2016 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric estimation of the hazard rate function, based on data modified by left truncation and/or right censoring, is considered. The hazard rate is not integrable over its support and hence it is traditionally estimated over a fixed interval under the mean integrated squared error (MISE) criterion. It is well known in the literature that neither left truncation nor right censoring affect the rate of the MISE convergence, but so far no results on how the modified data and the interval of estimation affect the MISE convergence have been known. To understand the affect, asymptotic theory of sharp minimax estimation is developed which indicates how the modified data and the interval of estimation affect the MISE convergence. The theory is complemented by presenting a data-driven estimator for small samples which is tested on numerical simulations and real data.
Book Synopsis The Statistical Analysis of Interval-censored Failure Time Data by : Jianguo Sun
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.
Book Synopsis Interval-Censored Time-to-Event Data by : Ding-Geng (Din) Chen
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 426 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.Divid
Book Synopsis Survival Analysis by : John P. Klein
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.
Book Synopsis Survival Analysis with Interval-Censored Data by : Kris Bogaerts
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.
Book Synopsis Survival Analysis: State of the Art by : John P. Klein
Download or read book Survival Analysis: State of the Art written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.
Book Synopsis Survival Analysis of Complex Featured Data with Measurement Error by : Li-Pang Chen
Download or read book Survival Analysis of Complex Featured Data with Measurement Error written by Li-Pang Chen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis plays an important role in many fields, such as cancer research, clinical trials, epidemiological studies, actuarial science, and so on. A large body of methods on analyzing survival data have been developed. However, many important problems have still not been fully explored. In this thesis, we focus on the analysis of survival data with complex features. In Chapter 1, we review relevant topics including survival analysis, the measurement error model, the graphical model, and variable selection. Graphical models are useful in characterizing the dependence structure of variables. They have been commonly used for analysis of high-dimensional data, including genetic data and data with network structures. Many estimation procedures have been developed under various graphical models with a stringent assumption that the associated variables must be measured precisely. In applications, this assumption, however, is often unrealistic and mismeasurement in variables is usually presented in data. In Chapter 2, we investigate the high-dimensional graphical model with error-prone variables. We propose valid estimation procedures to account for measurement error effects. Theoretical results are established for the proposed methods and numerical studies are reported to assess the performance of our proposed methods. In Chapter 3, we consider survival analysis with network structures and measurement error in covariates. In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical proportional hazards measurement error models, and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods. In Chapter 4, we focus on sufficient dimension reduction for high-dimensional survival data with covariate measurement error. Sufficient dimension reduction (SDR) is an important tool in regression analysis which reduces the dimension of covariates without losing predictive information. Several methods have been proposed to handle data with either censoring in the response or measurement error in covariates. However, little research is available to deal with data having these two features simultaneously. Moreover, the analysis becomes more challenging when data contain ultrahigh-dimensional covariates. In Chapter 4, we examine this problem. We start with considering the cumulative distribution function in regular settings and propose a valid SDR method to incorporate the effects of both censored data and covariates measurement error. Next, we extend the proposed method to handle ultrahigh-dimensional data. Theoretical results of the proposed methods are established. Numerical studies are reported to assess the performance of the proposed methods. In Chapter 5, we slightly switch our attention to examine sampling issues concerning survival data. Specifically, we discuss survival analysis for left-truncated and right-censored data with covariate measurement error. Many methods have been developed for analyzing survival data which commonly involve right-censoring. These methods, however, are challenged by complex features pertinent to the data collection as well as the nature of data themselves. Typically, biased samples caused by left-truncation or length-biased sampling and measurement error are often accompanying with survival analysis. While such data frequently arise in practice, little work has been available in the literature. In Chapter 5, we study this important problem and explore valid inference methods for handling left-truncated and right-censored survival data with measurement error under the widely used Cox model. We exploit a flexible estimator for the survival model parameters which does not require specification of the baseline hazard function. To improve the efficiency, we further develop an augmented non-parametric maximum likelihood estimator. We establish asymptotic results for the proposed estimators and examine the efficiency and robustness issues of the proposed estimators. The proposed methods enjoy appealing features that the distributions of the covariates and of the truncation times are left unspecified. Numerical studies are reported to assess the performance of the proposed methods. In Chapter 6, we study outstanding issues on model selection and model averaging for survival data with measurement error. Model selection plays a critical role in statistical inference and a vast literature has been devoted to this topic. Despite extensive research attention on model selection, research gaps still remain. An important but unexplored problem concerns model selection for truncated and censored data with measurement error. Although analysis of left-truncated and right-censored (LTRC) data has received extensive interests in survival analysis, there has been no research on model selection for LTRC data, let alone LTRC data involving with measurement error. In Chapter 6, we take up this important problem and develop inferential procedures to handle model selection for LTRC data with measurement error in covariates. Our development employs the local model misspecification framework and emphasizes the use of the focus information criterion (FIC). We develop valid estimators using the model averaging scheme and establish theoretical results to justify the validity of our methods. Numerical studies are conducted to assess the performance of the proposed methods. Finally, Chapter 7 summarizes the thesis with discussions.
Book Synopsis Maximum Likeihood Estimation and Covariate Analysis Based on Censored Survival Data by : Stephen W. Mykytyn
Download or read book Maximum Likeihood Estimation and Covariate Analysis Based on Censored Survival Data written by Stephen W. Mykytyn and published by . This book was released on 1980 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Density and Hazard Rate Estimation for Right Censored Data Using Wavelet Methods by : Anestis Antoniadis
Download or read book Density and Hazard Rate Estimation for Right Censored Data Using Wavelet Methods written by Anestis Antoniadis and published by . This book was released on 1997 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Hazard Function Estimation from Doubly-censored Data with Application to AIDS by : Robert Elliot Fusaro
Download or read book Nonparametric Hazard Function Estimation from Doubly-censored Data with Application to AIDS written by Robert Elliot Fusaro and published by . This book was released on 1992 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Survival Analysis Using S by : Mara Tableman
Download or read book Survival Analysis Using S written by Mara Tableman and published by CRC Press. This book was released on 2003-07-28 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Comparison of Estimation Procedures for the Linear, Hazard-rate Distribution by : Yusof Bin Mohamed Taib
Download or read book A Comparison of Estimation Procedures for the Linear, Hazard-rate Distribution written by Yusof Bin Mohamed Taib and published by . This book was released on 1982 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main concern of the research has been the estimation of the two parameters of the linear hazard rate distribution, both from complete and from singly censored samples. Firstly, easy graphical procedures are investigated. The hazard plot is found to be advantageous when both parameters are considered jointly. When the parameters are considered individually, the slope parameter is estimated with smallest mean square error by the hazard plot, while the intercept is estimated with smallest mean square error by plotting the logarithm of the survivor function. Simple smoothing procedures, such as running medians or moving averages, facilitate the fitting of lines by eye to these plots. Six analytical procedures are then proposed, investigated, and compared with each other and with existing procedures. These comparisons are based on extensive Monte Carlo calculations.........
Book Synopsis Analysis of Censored Data by : Hira L. Koul
Download or read book Analysis of Censored Data written by Hira L. Koul and published by IMS. This book was released on 1995 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation of Dependence Measures and Hazards from Bivariate Censored Data by : Zhongwei Zhang
Download or read book Estimation of Dependence Measures and Hazards from Bivariate Censored Data written by Zhongwei Zhang and published by . This book was released on 1996 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis MULTIVARIATE POPULATION ATTRIBUTABLE HAZARD FUNCTION FOR RIGHT-CENSORED DATA by : Vladimir Son
Download or read book MULTIVARIATE POPULATION ATTRIBUTABLE HAZARD FUNCTION FOR RIGHT-CENSORED DATA written by Vladimir Son and published by . This book was released on 2013 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measuring the impact to different risk factors on a disease at the population level is an important issue in public health, which necessitates the use of Population Attributable Risk (PAR). Traditionally, methods estimating PAR have been focusing on case-control or cross-sectional studies. Although a method for estimation of PAR in cohort study that properly takes into account right-censoring time was proposed recently, the estimation of time-dependent PAR, however, has not been developed completely. In this dissertation, we explore the estimation problem of PAR with several risk factors or possible confounders for right-censored data. By partitioning the covariate space, we extend the method of attributable hazard function (AHF) to account for several risk factors. In the situation when individual AHF can be partially ordered, we develop new stepwise multiple comparisons procedure using the Partitioning Principle. The simulation study confirms the proof that the proposed procedure always strongly controls the familywise error rate. Another highlight of the dissertation focuses on the asymptotic distribution of AHF with the assumption that the distribution of a risk factor is time-independent, under Cox model. When there are confounding variables, the adjusted AHF based on the case-load weighting approach is also derived. The new proposed methods are applied to analyze real-life data set in the arthritis study.