Empirical Likelihood Method In Survival Analysis PDF Download
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Author | : Mai Zhou |
Publisher | : CRC Press |
Total Pages | : 221 |
Release | : 2015-06-17 |
Genre | : Mathematics |
ISBN | : 1466554932 |
Download Empirical Likelihood Method in Survival Analysis Book in PDF, ePub and Kindle
Empirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN. The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empirical likelihood for censored data. It also covers semi-parametric accelerated failure time models, the optimality of confidence regions derived from empirical likelihood or plug-in empirical likelihood ratio tests, and several empirical likelihood confidence band results. While survival analysis is a classic area of statistical study, the empirical likelihood methodology has only recently been developed. Until now, just one book was available on empirical likelihood and most statistical software did not include empirical likelihood procedures. Addressing this shortfall, this book provides the functions to calculate the empirical likelihood ratio in survival analysis as well as functions related to the empirical likelihood analysis of the Cox regression model and other hazard regression models.
Author | : Hui Fang |
Publisher | : |
Total Pages | : 238 |
Release | : 2000 |
Genre | : |
ISBN | : |
Download Binomial Empirical Likelihood Ratio Method in Survival Analysis Book in PDF, ePub and Kindle
Author | : Art B. Owen |
Publisher | : CRC Press |
Total Pages | : 322 |
Release | : 2001-05-18 |
Genre | : Mathematics |
ISBN | : 1420036157 |
Download Empirical Likelihood Book in PDF, ePub and Kindle
Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It al
Author | : Bin He |
Publisher | : |
Total Pages | : 71 |
Release | : 2006 |
Genre | : Proportional hazards models |
ISBN | : |
Download Application of the Empirical Likelihood Method in Proportional Hazards Model Book in PDF, ePub and Kindle
Key words: Bootstrap, confidence interval, Cox model, doubly censored data, empirical likelihood function, goodness-of-fit test, maximum likelihood, partly interval-censored data, proportional hazards model, right censored data, survival analysis.
Author | : Albert Vexler |
Publisher | : CRC Press |
Total Pages | : 149 |
Release | : 2018-09-03 |
Genre | : Mathematics |
ISBN | : 1351001507 |
Download Empirical Likelihood Methods in Biomedicine and Health Book in PDF, ePub and Kindle
Empirical Likelihood Methods in Biomedicine and Health provides a compendium of nonparametric likelihood statistical techniques in the perspective of health research applications. It includes detailed descriptions of the theoretical underpinnings of recently developed empirical likelihood-based methods. The emphasis throughout is on the application of the methods to the health sciences, with worked examples using real data. Provides a systematic overview of novel empirical likelihood techniques. Presents a good balance of theory, methods, and applications. Features detailed worked examples to illustrate the application of the methods. Includes R code for implementation. The book material is attractive and easily understandable to scientists who are new to the research area and may attract statisticians interested in learning more about advanced nonparametric topics including various modern empirical likelihood methods. The book can be used by graduate students majoring in biostatistics, or in a related field, particularly for those who are interested in nonparametric methods with direct applications in Biomedicine.
Author | : Kaitai Fang |
Publisher | : World Scientific |
Total Pages | : 470 |
Release | : 2005 |
Genre | : Mathematics |
ISBN | : 9812567763 |
Download Contemporary Multivariate Analysis and Design of Experiments Book in PDF, ePub and Kindle
Index. Subject index -- Author index
Author | : Ali Jinnah |
Publisher | : |
Total Pages | : |
Release | : 2007 |
Genre | : Mortality |
ISBN | : |
Download Inference for Cox's Regression Model Via a New Version of Empirical Likelihood Book in PDF, ePub and Kindle
Cox Proportional Hazard Model is one of the most popular tools used in the study of Survival Analysis. Empirical Likelihood (EL) method has been used to study the Cox Proportional Hazard Model. In recent work by Qin and Jing (2001), empirical likelihood based confidence region is constructed with the assumption that the baseline hazard function is known. However, in Cox's regression model the baseline hazard function is unspecified. In this thesis, we re-formulate empirical likelihood for the vector of regression parameters by estimating the baseline hazard function. The EL confidence regions are obtained accordingly. In addition, Adjusted Empirical Likelihood (AEL) method is proposed. Furthermore, we conduct extensive simulation studies to evaluate the performance of the proposed empirical likelihood methods in terms of coverage probabilities by comparing with the Normal Approximation based method. The simulation studies show that all the three methods produce similar coverage probabilities.
Author | : M.S. Nikulin |
Publisher | : Springer Science & Business Media |
Total Pages | : 566 |
Release | : 2013-11-11 |
Genre | : Mathematics |
ISBN | : 0817682066 |
Download Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life Book in PDF, ePub and Kindle
Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields. Specific topics covered include: * cancer prognosis using survival forests * short-term health problems related to air pollution: analysis using semiparametric generalized additive models * semiparametric models in the studies of aging and longevity This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.
Author | : Jun Ma |
Publisher | : CRC Press |
Total Pages | : 401 |
Release | : 2024-10-01 |
Genre | : Mathematics |
ISBN | : 1351109707 |
Download Likelihood Methods in Survival Analysis Book in PDF, ePub and Kindle
Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes. Features Provides a broad and accessible overview of likelihood methods in survival analysis Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks Includes many examples using real data to illustrate the methods Includes integrated R code for implementation of the methods Supplemented by a GitHub repository with datasets and R code The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.
Author | : Mark Chang |
Publisher | : CRC Press |
Total Pages | : 218 |
Release | : 2019-03-20 |
Genre | : Mathematics |
ISBN | : 1351214527 |
Download Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials Book in PDF, ePub and Kindle
"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development.... Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.