June 16, 2021

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Analysis for Time-to-event Data Under Censoring and Truncation

Analysis for Time-to-event Data Under Censoring and Truncation
Author : Hongsheng Dai,Huan Wang
Publisher : Academic Press
Release Date : 2016-10-01
Category :
Total pages :96
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"Survival Analysis for Bivariate Truncated Data" provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection biasReviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival functionOffers a guideline for analyzing truncated survival data

Analysis for Time-to-Event Data under Censoring and Truncation

Analysis for Time-to-Event Data under Censoring and Truncation
Author : Hongsheng Dai,Huan Wang
Publisher : Academic Press
Release Date : 2016-10-06
Category : Mathematics
Total pages :102
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Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data

Survival Analysis

Survival Analysis
Author : John P. Klein,Melvin L. Moeschberger
Publisher : Springer Science & Business Media
Release Date : 2006-05-17
Category : Medical
Total pages :538
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Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, 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. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.

Applied Survival Analysis Using R

Applied Survival Analysis Using R
Author : Dirk F. Moore
Publisher : Springer
Release Date : 2016-05-11
Category : Medical
Total pages :226
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Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.

Survival Analysis Using S

Survival Analysis Using S
Author : Mara Tableman,Jong Sung Kim
Publisher : CRC Press
Release Date : 2003-07-28
Category : Mathematics
Total pages :280
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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.

Journal of the American Statistical Association

Journal of the American Statistical Association
Author : Anonim
Publisher : Unknown
Release Date : 2005
Category : Statistics
Total pages :129
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Encyclopedia of Measurement and Statistics

Encyclopedia of Measurement and Statistics
Author : Neil J. Salkind
Publisher : SAGE Publications
Release Date : 2006-10-13
Category : Social Science
Total pages :1416
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The Encyclopedia of Measurement and Statistics presents state-of-the-art information and ready-to-use facts from the fields of measurement and statistics in an unintimidating style. The ideas and tools contained in these pages are approachable and can be invaluable for understanding our very technical world and the increasing flow of information. Although there are references that cover statistics and assessment in depth, none provides as comprehensive a resource in as focused and accessible a manner as the three volumes of this Encyclopedia. Through approximately 500 contributions, experts provide an overview and an explanation of the major topics in these two areas.

Analysis of Dependent Interval-censored Time-to-event Data

Analysis of Dependent Interval-censored Time-to-event Data
Author : Daohai Yu
Publisher : Unknown
Release Date : 2000
Category :
Total pages :129
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Data Mining VI

Data Mining VI
Author : A. Zanasi,C. A. Brebbia,Nelson F. F. Ebecken
Publisher : Wit Pr/Computational Mechanics
Release Date : 2005
Category : Computers
Total pages :550
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This book contains most of the papers presented at the Sixth International Conference on Data Mining held in Skiathos, Greece. Twenty-five countries from all the continents are represented in the papers published in the book, offering a real multinational and multicultural range of experiences and ideas.

Cancer Epidemiology

Cancer Epidemiology
Author : Mukesh Verma
Publisher : Unknown
Release Date : 2009
Category : Cancer
Total pages :129
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Proceedings of the Statistical Computing Section

Proceedings of the Statistical Computing Section
Author : American Statistical Association. Statistical Computing Section
Publisher : Unknown
Release Date : 1996
Category : Mathematical statistics
Total pages :129
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Current Index to Statistics, Applications, Methods and Theory

Current Index to Statistics, Applications, Methods and Theory
Author : Anonim
Publisher : Unknown
Release Date : 1998
Category : Mathematical statistics
Total pages :129
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The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.

Technometrics

Technometrics
Author : Anonim
Publisher : Unknown
Release Date : 2004
Category : Experimental design
Total pages :129
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Biostatistics

Biostatistics
Author : Gerald van Belle,Lloyd D. Fisher,Patrick J. Heagerty,Thomas Lumley
Publisher : Wiley-Interscience
Release Date : 2004-07-26
Category : Medical
Total pages :896
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A respected introduction to biostatistics, thoroughly updated and revised The first edition of Biostatistics: A Methodology for the Health Sciences has served professionals and students alike as a leading resource for learning how to apply statistical methods to the biomedical sciences. This substantially revised Second Edition brings the book into the twenty-first century for today’s aspiring and practicing medical scientist. This versatile reference provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Written with an eye toward the use of computer applications, the book examines the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference; explores more advanced statistical methods; and illustrates important current uses of biostatistics. New to this edition are discussions of Longitudinal data analysis Randomized clinical trials Bayesian statistics GEE The bootstrap method Enhanced by a companion Web site providing data sets, selected problems and solutions, and examples from such current topics as HIV/AIDS, this is a thoroughly current, comprehensive introduction to the field.

Survival Analysis Via Nonparametric Multiple Imputation

Survival Analysis Via Nonparametric Multiple Imputation
Author : Chiu-Hsieh Hsu
Publisher : Unknown
Release Date : 2003
Category :
Total pages :129
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