Analysis Of Complex Life History Data And Variable Selection In Survival Analysis Under Interval Censoring
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Analysis Of Complex Life History Data And Variable Selection In Survival Analysis Under Interval Censoring
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Author : Daewoo Pak
language : en
Publisher:
Release Date : 2018
Analysis Of Complex Life History Data And Variable Selection In Survival Analysis Under Interval Censoring written by Daewoo Pak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electronic dissertations categories.
Survival Analysis With Interval Censored Data
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Author : Kris Bogaerts
language : en
Publisher: CRC Press
Release Date : 2017-11-20
Survival Analysis With Interval Censored Data written by Kris Bogaerts and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-20 with Mathematics categories.
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.
Variable Selection And Prediction For Complex Survival Data Analysis
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Author : Xiaowei Ren
language : en
Publisher:
Release Date : 2017
Variable Selection And Prediction For Complex Survival Data Analysis written by Xiaowei Ren and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Survival analysis methods for time-to-event data are commonly used in biomedical researches. It is essential to select the important variables and identify the correct covariate functional form. After selection of important variables, it is of interest to evaluate the prediction performance of the selected model, typically by receiver oper ating characteristic (ROC) curve. Furthermore, the analysis of time-to-event data is complicated by the presence of interval censoring and dependent competing events, both of which occur frequently in clinical studies. In this dissertation, we set to de velop variable selection and prediction methods for complex survival data. In the first topic, we proposed a two-stage procedure to identify the linear and/or non-linear co variates functional forms simultaneously and estimate the selected covariate effects for competing risks data. Spectral decomposition was used to decompose the nonpara metric covariate function. The adaptive LASSO method was then to select the linear and non-linear components, respectively. We showed that our method achieved good selection accuracy and minimal estimation biases. In the second topic, to evaluate the prediction performance, we extended the ROC function estimation of right-censored competing risks data to interval-censored data. We proved the consistency of the estimator and demonstrated the convergence of estimator in numerical studies. In the third topic, we extended the ROC function for independent survival data to clustered survival data using within-cluster-resampling (WCR) technique. All the three methods had been implemented in real data as illustration.
Survival Analysis
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Author : John P. Klein
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Survival Analysis written by John P. Klein and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Medical categories.
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.
Survival Analysis In Medicine And Genetics
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Author : Jialiang Li
language : en
Publisher: CRC Press
Release Date : 2013-06-04
Survival Analysis In Medicine And Genetics written by Jialiang Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-04 with Mathematics categories.
Using real data sets throughout, this text introduces the latest methods for analyzing high-dimensional survival data. With an emphasis on the applications of survival analysis techniques in genetics, it presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. The book reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.
Model Evaluation And Variable Selection For Interval Censored Data
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Author : Tyler Cook
language : en
Publisher:
Release Date : 2015
Model Evaluation And Variable Selection For Interval Censored Data written by Tyler Cook and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Survival analysis is a popular area of statistics dealing with time-to-event data. This type of data can be seen in many disciplines, but it is perhaps most commonly encountered in medical studies. Doctors, for example, might be testing different treatments developed to prolong the lifetimes of cancer patients. Unfortunately, in practical problems such as clinical trials, there is often incomplete data thanks to patients dropping out of the study. This results in censoring, which is a special characteristic of survival data. There are many different types of censoring. This dissertation focuses on the analysis of interval-censored data, where the failure time is only known to belong to some interval of observation times. One problem that researchers face when analyzing survival data is how to handle the censoring distribution. It is often assumed that the observation process generating the censoring is independent of the event time of interest. Consequently, the observation process can effectively be ignored. However, this assumption is clearly not always realistic. Unfortunately, one cannot generally test for independent censoring without additional assumptions or information. Therefore, the researcher is faced with a choice between using methods designed for informative or noninformative censoring. Chapters 2 and 3 of this dissertation investigate the effectiveness of different methods developed for the analysis of informative case I and case II interval censored data under both types of censoring. Extensive simulation studies indicate that the methods produce unbiased results in the presence of both informative and noninformative censoring. The efficiency of the informative censoring methods is then compared with approaches created to handle noninformative censoring. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the dependence of the censoring distribution. Another important problem seen in survival analysis is determining the set of predictors that are significantly related with the failure time being studied. Variable selection has received substantial attention both in classical linear models as well as survival analysis. This is largely thanks to recent technological advances making it easier for researchers in biology to collect huge amounts of genetic data. For example, a researcher with access to gene expression levels for hundreds of genes is interested in identifying which of those genes can predict tumor development time in cancer patients. One must sift through the large number of genes in order to find the small set of significant genes that influence tumor growth. Several methods using penalized likelihood procedures have been proposed to perform parameter estimation and variable selection simultaneously. A number of these techniques have also been extended to the case of right-censored survival data, but little has been done in the context of interval-censoring. In chapter 4, we propose an imputation approach for variable selection of interval-censored data that utilizes these penalized likelihood procedures. This method uses imputation to create a new dataset of imputed exact failure times and right-censored observations. Variable selection can then be performed on the imputed dataset using any of the popular variable selection techniques created for right-censored data. Comprehensive simulation studies illustrate the effectiveness of this new approach. Also, this method is attractive due to how easy it is to implement, since it can take advantage of existing software for variable selection of right-censored data.
Interval Censored Time To Event Data
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Author : Ding-Geng (Din) Chen
language : en
Publisher: CRC Press
Release Date : 2012-07-19
Interval Censored Time To Event Data written by Ding-Geng (Din) Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-19 with Mathematics categories.
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
Handbook Of Survival Analysis
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Author : John P. Klein
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Handbook Of Survival Analysis written by John P. Klein and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
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
Analysis Of Survival Data
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Author : D.R. Cox
language : en
Publisher: CRC Press
Release Date : 1984-06-01
Analysis Of Survival Data written by D.R. Cox and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984-06-01 with Mathematics categories.
This monograph contains many ideas on the analysis of survival data to present a comprehensive account of the field. The value of survival analysis is not confined to medical statistics, where the benefit of the analysis of data on such factors as life expectancy and duration of periods of freedom from symptoms of a disease as related to a treatment applied individual histories and so on, is obvious. The techniques also find important applications in industrial life testing and a range of subjects from physics to econometrics. In the eleven chapters of the book the methods and applications of are discussed and illustrated by examples.
The Statistical Analysis Of Interval Censored Failure Time Data
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Author : Jianguo Sun
language : en
Publisher: Springer
Release Date : 2007-05-26
The Statistical Analysis Of Interval Censored Failure Time Data written by Jianguo Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-26 with Mathematics categories.
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.