Analysis for Time to event Data Under Censoring and Truncation

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  • Author : Hongsheng Dai
  • Publisher : Academic Press
  • Pages : 96 pages
  • ISBN : 9780128054802
  • Rating : /5 from reviews
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Download or Read online Analysis for Time to event Data Under Censoring and Truncation full in PDF, ePub and kindle. this book written by Hongsheng Dai and published by Academic Press which was released on 01 October 2016 with total page 96 pages. We cannot guarantee that Analysis for Time to event Data Under Censoring and Truncation book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. "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 : 01 October 2016
GET THIS BOOK Analysis for Time to event Data Under Censoring and Truncation

"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

Survival Analysis

Survival Analysis
  • Author : John P. Klein,Melvin L. Moeschberger
  • Publisher : Springer Science & Business Media
  • Release : 17 May 2006
GET THIS BOOK Survival Analysis

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

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 : 06 October 2016
GET THIS BOOK Analysis for Time to Event Data under Censoring and Truncation

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

Survival Analysis Using S

Survival Analysis Using S
  • Author : Mara Tableman,Jong Sung Kim
  • Publisher : CRC Press
  • Release : 28 July 2003
GET THIS BOOK Survival Analysis Using S

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.

Applied Survival Analysis Using R

Applied Survival Analysis Using R
  • Author : Dirk F. Moore
  • Publisher : Springer
  • Release : 11 May 2016
GET THIS BOOK Applied Survival Analysis Using R

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

Advanced Survival Models

Advanced Survival Models
  • Author : Catherine Legrand
  • Publisher : CRC Press
  • Release : 23 March 2021
GET THIS BOOK Advanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when

Reliability and Survival Analysis

Reliability and Survival Analysis
  • Author : Md. Rezaul Karim,M. Ataharul Islam
  • Publisher : Springer
  • Release : 09 August 2019
GET THIS BOOK Reliability and Survival Analysis

This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit

Applied Categorical and Count Data Analysis

Applied Categorical and Count Data Analysis
  • Author : Wan Tang,Hua He,Xin M. Tu
  • Publisher : CRC Press
  • Release : 04 June 2012
GET THIS BOOK Applied Categorical and Count Data Analysis

Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments. The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that

Interval Censored Time to Event Data

Interval Censored Time to Event Data
  • Author : Ding-Geng (Din) Chen,Jianguo Sun,Karl E. Peace
  • Publisher : CRC Press
  • Release : 19 July 2012
GET THIS BOOK Interval Censored Time to Event Data

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. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current

Survival Analysis with Interval Censored Data

Survival Analysis with Interval Censored Data
  • Author : Kris Bogaerts,Arnost Komarek,Emmanuel Lesaffre
  • Publisher : CRC Press
  • Release : 20 November 2017
GET THIS BOOK Survival Analysis with Interval Censored Data

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.

Survival Analysis

Survival Analysis
  • Author : John P. Klein,Melvin L. Moeschberger
  • Publisher : Springer Science & Business Media
  • Release : 29 June 2013
GET THIS BOOK Survival Analysis

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

Applied Survival Analysis

Applied Survival Analysis
  • Author : David W. Hosmer, Jr.,Stanley Lemeshow,Susanne May
  • Publisher : John Wiley & Sons
  • Release : 23 September 2011
GET THIS BOOK Applied Survival Analysis

THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study.

Modeling Discrete Time to Event Data

Modeling Discrete Time to Event Data
  • Author : Gerhard Tutz,Matthias Schmid
  • Publisher : Springer
  • Release : 14 June 2016
GET THIS BOOK Modeling Discrete Time to Event Data

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because

Data Analysis with Competing Risks and Intermediate States

Data Analysis with Competing Risks and Intermediate States
  • Author : Ronald B. Geskus
  • Publisher : CRC Press
  • Release : 14 July 2015
GET THIS BOOK Data Analysis with Competing Risks and Intermediate States

Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.After introducing example studies from the biomedical and