Download Analysis for Time-to-Event Data under Censoring and Truncation Ebook PDF

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

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

by Hongsheng Dai,Huan Wang

  • Publisher : Academic Press
  • Release : 2016-10-01
  • Pages : 96
  • ISBN : 9780128054802
  • Language : En, Es, Fr & De
GET BOOK

"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

Survival Analysis

Survival Analysis
Techniques for Censored and Truncated Data

by John P. Klein,Melvin L. Moeschberger

  • Publisher : Springer Science & Business Media
  • Release : 2006-05-17
  • Pages : 538
  • ISBN : 0387216456
  • Language : En, Es, Fr & De
GET BOOK

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.

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

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

by Hongsheng Dai,Huan Wang

  • Publisher : Academic Press
  • Release : 2016-10-06
  • Pages : 102
  • ISBN : 0081010087
  • Language : En, Es, Fr & De
GET BOOK

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 Using S

Survival Analysis Using S
Analysis of Time-to-Event Data

by Mara Tableman,Jong Sung Kim

  • Publisher : CRC Press
  • Release : 2003-07-28
  • Pages : 280
  • ISBN : 9780203501412
  • Language : En, Es, Fr & De
GET BOOK

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.

Applied Survival Analysis Using R

Applied Survival Analysis Using R
A Book

by Dirk F. Moore

  • Publisher : Springer
  • Release : 2016-05-11
  • Pages : 226
  • ISBN : 3319312456
  • Language : En, Es, Fr & De
GET BOOK

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.

Journal of the American Statistical Association

Journal of the American Statistical Association
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2005
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Advanced Survival Models

Advanced Survival Models
A Book

by Catherine Legrand

  • Publisher : CRC Press
  • Release : 2021-03-23
  • Pages : 360
  • ISBN : 0429622554
  • Language : En, Es, Fr & De
GET BOOK

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 a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

Modeling Discrete Time-to-Event Data

Modeling Discrete Time-to-Event Data
A Book

by Gerhard Tutz,Matthias Schmid

  • Publisher : Springer
  • Release : 2016-06-14
  • Pages : 247
  • ISBN : 3319281585
  • Language : En, Es, Fr & De
GET BOOK

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 they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

Reliability and Survival Analysis

Reliability and Survival Analysis
A Book

by Md. Rezaul Karim,M. Ataharul Islam

  • Publisher : Springer
  • Release : 2019-08-09
  • Pages : 252
  • ISBN : 9811397767
  • Language : En, Es, Fr & De
GET BOOK

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 students, researchers, and practitioners in reliability and survival analysis, reliability engineering, biostatistics, and the biomedical sciences.

Applied Survival Analysis

Applied Survival Analysis
Regression Modeling of Time-to-Event Data

by David W. Hosmer, Jr.,Stanley Lemeshow,Susanne May

  • Publisher : John Wiley & Sons
  • Release : 2011-09-23
  • Pages : 416
  • ISBN : 1118211588
  • Language : En, Es, Fr & De
GET BOOK

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. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

Interval-Censored Time-to-Event Data

Interval-Censored Time-to-Event Data
Methods and Applications

by Ding-Geng (Din) Chen,Jianguo Sun,Karl E. Peace

  • Publisher : CRC Press
  • Release : 2012-07-19
  • Pages : 434
  • ISBN : 1466504250
  • Language : En, Es, Fr & De
GET BOOK

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 status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.

Applied Categorical and Count Data Analysis

Applied Categorical and Count Data Analysis
A Book

by Wan Tang,Hua He,Xin M. Tu

  • Publisher : CRC Press
  • Release : 2012-06-04
  • Pages : 384
  • ISBN : 143989793X
  • Language : En, Es, Fr & De
GET BOOK

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 include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.

Encyclopedia of Measurement and Statistics

Encyclopedia of Measurement and Statistics
A Book

by Neil J. Salkind

  • Publisher : SAGE Publications
  • Release : 2006-10-13
  • Pages : 1416
  • ISBN : 1506319955
  • Language : En, Es, Fr & De
GET BOOK

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.

Survival Analysis

Survival Analysis
Techniques for Censored and Truncated Data

by John P. Klein,Melvin L. Moeschberger

  • Publisher : Springer Science & Business Media
  • Release : 2013-06-29
  • Pages : 502
  • ISBN : 1475727283
  • Language : En, Es, Fr & De
GET BOOK

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 with Interval-Censored Data

Survival Analysis with Interval-Censored Data
A Practical Approach with Examples in R, SAS, and BUGS

by Kris Bogaerts,Arnost Komarek,Emmanuel Lesaffre

  • Publisher : CRC Press
  • Release : 2017-11-20
  • Pages : 584
  • ISBN : 1420077481
  • Language : En, Es, Fr & De
GET BOOK

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.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III

by Frank Hutter

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030676641
  • Language : En, Es, Fr & De
GET BOOK

Data Analysis with Competing Risks and Intermediate States

Data Analysis with Competing Risks and Intermediate States
A Book

by Ronald B. Geskus

  • Publisher : CRC Press
  • Release : 2015-07-14
  • Pages : 277
  • ISBN : 1466570369
  • Language : En, Es, Fr & De
GET BOOK

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

Joint Models for Longitudinal and Time-to-Event Data

Joint Models for Longitudinal and Time-to-Event Data
With Applications in R

by Dimitris Rizopoulos

  • Publisher : CRC Press
  • Release : 2012-06-22
  • Pages : 275
  • ISBN : 1439872872
  • Language : En, Es, Fr & De
GET BOOK

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/

Handbook of Infectious Disease Data Analysis

Handbook of Infectious Disease Data Analysis
A Book

by Leonhard Held,Niel Hens,Philip D O'Neill,Jacco Wallinga

  • Publisher : CRC Press
  • Release : 2019-11-04
  • Pages : 554
  • ISBN : 1351839314
  • Language : En, Es, Fr & De
GET BOOK

Recent years have seen an explosion in new kinds of data on infectious diseases, including data on social contacts, whole genome sequences of pathogens, biomarkers for susceptibility to infection, serological panel data, and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key features Contributors include many leading researchers in the field Divided into four main sections: Basic concepts, Analysis of Outbreak Data, Analysis of Seroprevalence Data, Analysis of Surveillance Data Numerous case studies and examples throughout Provides both introductory material and key reference material

Semiparametric Methods for Analysis of Randomized Clinical Trials and Arbitrarily Censored Time-to-event Data

Semiparametric Methods for Analysis of Randomized Clinical Trials and Arbitrarily Censored Time-to-event Data
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2004
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

This dissertation includes two parts. In part one, using the theory of semiparametrics, we develop a general approach to improving efficiency of nferences in randomized clinical trials using auxiliary covariates. In part two, we study "smooth" semiparametric regression analysis for arbitrarily censored time-to-event data. The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two-arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds-ratios or log-odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly-applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods. A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild "smoothness" conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a "parametric" representation, w.