Models For Multi State Survival Data

Models For Multi State Survival Data Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Models For Multi State Survival Data book. This book definitely worth reading, it is an incredibly well-written.

Multi-State Survival Models for Interval-Censored Data

Ardo van den Hout
Multi State Survival Models for Interval Censored Data Book PDF

Author : Ardo van den Hout
Publisher : CRC Press
Page : 181 pages
File Size : 41,5 Mb
Release : 2016-11-25
Category : Mathematics
ISBN : 9781315356730

Get Book

Multi-State Survival Models for Interval-Censored Data by Ardo van den Hout Pdf

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

Models for Multi-State Survival Data

Per Kragh Andersen,Henrik Ravn
Models for Multi State Survival Data Book PDF

Author : Per Kragh Andersen,Henrik Ravn
Publisher : CRC Press
Page : 293 pages
File Size : 45,7 Mb
Release : 2023-10-11
Category : Mathematics
ISBN : 9780429642265

Get Book

Models for Multi-State Survival Data by Per Kragh Andersen,Henrik Ravn Pdf

Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and public health. The book includes mathematical detail which can be skipped by readers more interested in the practical examples. It is aimed at biostatisticians and at readers with an interest in the topic having a more applied background, such as epidemiology. This book builds on several courses the authors have taught on the subject. Key Features: · Intensity-based and marginal models. · Survival data, competing risks, illness-death models, recurrent events. · Includes a full chapter on pseudo-values. · Intuitive introductions and mathematical details. · Practical examples of event history data. · Exercises. Software code in R and SAS and the data used in the book can be found on the book’s webpage.

Competing Risks and Multistate Models with R

Jan Beyersmann,Arthur Allignol,Martin Schumacher
Competing Risks and Multistate Models with R Book PDF

Author : Jan Beyersmann,Arthur Allignol,Martin Schumacher
Publisher : Springer Science & Business Media
Page : 249 pages
File Size : 45,9 Mb
Release : 2011-11-18
Category : Mathematics
ISBN : 9781461420354

Get Book

Competing Risks and Multistate Models with R by Jan Beyersmann,Arthur Allignol,Martin Schumacher Pdf

This book covers competing risks and multistate models, sometimes summarized as event history analysis. These models generalize the analysis of time to a single event (survival analysis) to analysing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on nonparametric methods.

Multi-State Survival Models for Interval-Censored Data

Ardo van den Hout
Multi State Survival Models for Interval Censored Data Book PDF

Author : Ardo van den Hout
Publisher : CRC Press
Page : 257 pages
File Size : 51,5 Mb
Release : 2016-11-25
Category : Mathematics
ISBN : 9781466568419

Get Book

Multi-State Survival Models for Interval-Censored Data by Ardo van den Hout Pdf

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

Introducing Survival and Event History Analysis

Melinda Mills
Introducing Survival and Event History Analysis Book PDF

Author : Melinda Mills
Publisher : SAGE
Page : 301 pages
File Size : 51,9 Mb
Release : 2011-01-19
Category : Social Science
ISBN : 9781848601024

Get Book

Introducing Survival and Event History Analysis by Melinda Mills Pdf

This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, conversations with key scholars and resources for both students and instructors, this text allows researchers to quickly master advanced statistical techniques. It is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook. Also included are up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text.

Multistate Models for the Analysis of Life History Data

Richard J Cook,Jerald F. Lawless
Multistate Models for the Analysis of Life History Data Book PDF

Author : Richard J Cook,Jerald F. Lawless
Publisher : CRC Press
Page : 524 pages
File Size : 42,7 Mb
Release : 2018-05-15
Category : Mathematics
ISBN : 9781351646055

Get Book

Multistate Models for the Analysis of Life History Data by Richard J Cook,Jerald F. Lawless Pdf

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research. It features: Discusses a wide range of applications of multistate models, Presents methods for both continuously and intermittently observed life history processes, Gives a thorough discussion of conditionally independent censoring and observation processes, Discusses models with random effects and joint models for two or more multistate processes, Discusses and illustrates software for multistate analysis that is available in R, Target audience includes those engaged in research and applications involving multistate models.

Analysis of Multivariate Survival Data

Philip Hougaard
Analysis of Multivariate Survival Data Book PDF

Author : Philip Hougaard
Publisher : Springer Science & Business Media
Page : 559 pages
File Size : 48,6 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461213048

Get Book

Analysis of Multivariate Survival Data by Philip Hougaard Pdf

Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.

Statistical Models Based on Counting Processes

Per K. Andersen,Ornulf Borgan,Richard D. Gill,Niels Keiding
Statistical Models Based on Counting Processes Book PDF

Author : Per K. Andersen,Ornulf Borgan,Richard D. Gill,Niels Keiding
Publisher : Springer Science & Business Media
Page : 779 pages
File Size : 44,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461243489

Get Book

Statistical Models Based on Counting Processes by Per K. Andersen,Ornulf Borgan,Richard D. Gill,Niels Keiding Pdf

Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, almost all the methods are given in concrete detail for use in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuarial mathematicians, reliability engineers and biologists). Much of the material has so far only been available in the journal literature (if at all), and so a wide variety of researchers will find this an invaluable survey of the subject.

Data Analysis with Competing Risks and Intermediate States

Ronald B. Geskus
Data Analysis with Competing Risks and Intermediate States Book PDF

Author : Ronald B. Geskus
Publisher : CRC Press
Page : 278 pages
File Size : 41,9 Mb
Release : 2015-07-14
Category : Mathematics
ISBN : 9781466570368

Get Book

Data Analysis with Competing Risks and Intermediate States by Ronald B. Geskus Pdf

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

Handbook of Survival Analysis

John P. Klein,Hans C. van Houwelingen,Joseph G. Ibrahim,Thomas H. Scheike
Handbook of Survival Analysis Book PDF

Author : John P. Klein,Hans C. van Houwelingen,Joseph G. Ibrahim,Thomas H. Scheike
Publisher : CRC Press
Page : 635 pages
File Size : 50,7 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781466555679

Get Book

Handbook of Survival Analysis by John P. Klein,Hans C. van Houwelingen,Joseph G. Ibrahim,Thomas H. Scheike Pdf

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

Multistate Analysis of Life Histories with R

Frans Willekens
Multistate Analysis of Life Histories with R Book PDF

Author : Frans Willekens
Publisher : Springer
Page : 323 pages
File Size : 47,6 Mb
Release : 2014-09-11
Category : Mathematics
ISBN : 9783319083834

Get Book

Multistate Analysis of Life Histories with R by Frans Willekens Pdf

This book provides an introduction to multistate event history analysis. It is an extension of survival analysis, in which a single terminal event (endpoint) is considered and the time-to-event is studied. Multistate models focus on life histories or trajectories, conceptualized as sequences of states and sequences of transitions between states. Life histories are modeled as realizations of continuous-time Markov processes. The model parameters, transition rates, are estimated from data on event counts and populations at risk, using the statistical theory of counting processes. The Comprehensive R Network Archive (CRAN) includes several packages for multistate modeling. This book is about Biograph. The package is designed to (a) enhance exploratory analysis of life histories and (b) make multistate modeling accessible. The package incorporates utilities that connect to several packages for multistate modeling, including survival, eha, Epi, mvna,, mstate, msm, and TraMineR for sequence analysis. The book is a ‘hands-on’ presentation of Biograph and the packages listed. It is written from the perspective of the user. To help the user master the techniques and the software, a single data set is used to illustrate the methods and software. It is the subsample of the German Life History Survey, which was also used by Blossfeld and Rohwer in their popular textbook on event history modeling. Another data set, the Netherlands Family and Fertility Survey, is used to illustrate how Biograph can assist in answering questions on life paths of cohorts and individuals. The book is suitable as a textbook for graduate courses on event history analysis and introductory courses on competing risks and multistate models. It may also be used as a self-study book. The R code used in the book is available online. Frans Willekens is affiliated with the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany. He is Emeritus Professor of Demography at the University of Groningen, a Honorary Fellow of the Netherlands Interdisciplinary Demographic Institute (NIDI) in the Hague, and a Research Associate of the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. He is a member of Royal Netherlands Academy of Arts and Sciences (KNAW). He has contributed to the modeling and simulation of life histories, mainly in the context of population forecasting.

Survival Analysis

Alejandro Quiroz Flores
Survival Analysis Book PDF

Author : Alejandro Quiroz Flores
Publisher : Cambridge University Press
Page : 136 pages
File Size : 54,6 Mb
Release : 2022-05-26
Category : Political Science
ISBN : 9781009062312

Get Book

Survival Analysis by Alejandro Quiroz Flores Pdf

Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional covariate effects, competing risks, and multi-state models. It also revisits models for repeated events. The Element promotes multi-state models as a unified framework for survival analysis and highlights the role of general transition probabilities as key quantities of interest that complement traditional hazard analysis. These quantities focus on the long term probabilities that units will occupy particular states conditional on their current state, and they are central in the design and implementation of policy interventions.

Dynamic Prediction in Clinical Survival Analysis

Hans van Houwelingen,Hein Putter
Dynamic Prediction in Clinical Survival Analysis Book PDF

Author : Hans van Houwelingen,Hein Putter
Publisher : CRC Press
Page : 250 pages
File Size : 43,8 Mb
Release : 2011-11-09
Category : Mathematics
ISBN : 9781439835432

Get Book

Dynamic Prediction in Clinical Survival Analysis by Hans van Houwelingen,Hein Putter Pdf

There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime a

The Statistical Analysis of Failure Time Data

John D. Kalbfleisch,Ross L. Prentice
The Statistical Analysis of Failure Time Data Book PDF

Author : John D. Kalbfleisch,Ross L. Prentice
Publisher : John Wiley & Sons
Page : 462 pages
File Size : 46,8 Mb
Release : 2011-01-25
Category : Mathematics
ISBN : 9781118031230

Get Book

The Statistical Analysis of Failure Time Data by John D. Kalbfleisch,Ross L. Prentice Pdf

Contains additional discussion and examples on left truncationas well as material on more general censoring and truncationpatterns. Introduces the martingale and counting process formulation swillbe in a new chapter. Develops multivariate failure time data in a separate chapterand extends the material on Markov and semi Markovformulations. Presents new examples and applications of data analysis.

Statistical Analysis with Measurement Error or Misclassification

Grace Y. Yi
Statistical Analysis with Measurement Error or Misclassification Book PDF

Author : Grace Y. Yi
Publisher : Springer
Page : 479 pages
File Size : 50,8 Mb
Release : 2017-08-02
Category : Mathematics
ISBN : 9781493966400

Get Book

Statistical Analysis with Measurement Error or Misclassification by Grace Y. Yi Pdf

This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.