Competing Risks And Multistate Models With R

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Competing Risks And Multistate Models With R

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Author : Jan Beyersmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-18

Competing Risks And Multistate Models With R written by Jan Beyersmann 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 2011-11-18 with Mathematics categories.

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.

Competing Risks And Multistate Models With R

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Author :
language : en
Publisher:
Release Date : 2012-01-21

Competing Risks And Multistate Models With R written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-21 with categories.

Data Analysis With Competing Risks And Intermediate States

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Author : Ronald B. Geskus
language : en
Publisher: CRC Press
Release Date : 2015-07-14

Data Analysis With Competing Risks And Intermediate States written by Ronald B. Geskus and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-14 with Mathematics categories.

This practical and thorough book 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. Each chapter includes standard exercises; a software section on SAS, Stata, and R; and computer practicals that allow readers to practice with the techniques. The book's website provides the R code for the computer practicals along with other material.

Multistate Analysis Of Life Histories With R

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Author : Frans Willekens
language : en
Publisher: Springer
Release Date : 2014-09-11

Multistate Analysis Of Life Histories With R written by Frans Willekens and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-11 with Mathematics categories.

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.

Introducing Survival And Event History Analysis

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Author : Melinda Mills
language : en
Publisher: SAGE
Release Date : 2011-01-19

Introducing Survival And Event History Analysis written by Melinda Mills and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-19 with Social Science categories.

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.

The Statistical Analysis Of Failure Time Data

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Author : John D. Kalbfleisch
language : en
Publisher: John Wiley & Sons
Release Date : 2002-09-09

The Statistical Analysis Of Failure Time Data written by John D. Kalbfleisch and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-09-09 with Mathematics categories.

* Contains additional discussion and examples on left truncation as well as material on more general censoring and truncation patterns. * Introduces the martingale and counting process formulation swil lbe in a new chapter. * Develops multivariate failure time data in a separate chapter and extends the material on Markov and semi Markov formulations. * Presents new examples and applications of data analysis.

Multistate Models For The Analysis Of Life History Data

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Author : Richard J Cook
language : en
Publisher: CRC Press
Release Date : 2018-05-15

Multistate Models For The Analysis Of Life History Data written by Richard J Cook and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with Mathematics categories.

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.

Modeling Discrete Time To Event Data

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Author : Gerhard Tutz
language : en
Publisher: Springer
Release Date : 2016-06-14

Modeling Discrete Time To Event Data written by Gerhard Tutz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-14 with Mathematics categories.

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.

R For Health Technology Assessment

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Author : Gianluca Baio
language : en
Publisher: CRC Press
Release Date : 2025-06-30

R For Health Technology Assessment written by Gianluca Baio and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Mathematics categories.

R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA). It has been designed with the objective of establishing the use of R as the standard tool for HTA amongst academics, industry practitioners and regulators. It covers a lot of ground, starting with the necessary background in HTA, R and statistical inference, followed by various modelling tools, ranging from missing data, survival analysis and decision trees, through to multistate models and discrete event simulation. The methods are all illustrated with many detailed worked examples and case studies using real data, and there are detailed descriptions of the code and processes. Key Features: Introductory chapters on the various topics of the book, including HTA, R and statistical inference A wide range of common analytical tools used in HTA, from modelling for individual-level data, missing data, survival analysis, decision-modelling and network meta-analysis More advanced and increasingly popular tools, such as those for population adjustment, discrete event simulation and the use of web applications as front-end for the overall statistical modelling Many detailed worked examples and case studies using real data to illustrate the methodology Fully integrated R code gives detailed guidance on implementation of the techniques Supplemented by a website with additional resources, including annotated code and data This text is primarily aimed at modellers working in the field of HTA, regulators and reviewers of reimbursement dossiers and cost-effectiveness analyses. It also complements a wide range of undergraduate and graduate programmes in HTA, health and public health economics, as well as academic researchers in the field of statistical modelling for HTA.

Stochastic Comparisons With Applications

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Author : Subhash C. Kochar
language : en
Publisher: Springer Nature
Release Date : 2022-10-22

Stochastic Comparisons With Applications written by Subhash C. Kochar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-22 with Mathematics categories.

This book emphasizes the use of stochastic orders as motivational tools for developing new statistical procedures. Stochastic orders have found useful applications in many disciplines, including reliability theory, survival analysis, risk theory, finance, nonparametric methods, economics and actuarial science. Written by a statistician, this volume clarifies the connection between stochastic orders and nonparametric methods. The importance of order statistics and spacings is well recognized. Classically, they mainly focus on the case when the observations are independent and identically distributed, however, several new developments have extended the comparison of order statistics to the case of non-identically distributed or non-independent observations. In addition to giving a detailed discussion of various topics in the general area of stochastic orders, a substantial part of the book is devoted to recent research on stochastic comparisons of order statistics and spacings, including a long chapter on dependence among them. The book will be useful for graduate students and researchers in statistics, economics, actuarial science and other related disciplines. In particular, with close to 300 references, it will be a valuable resource for reliability theorists, applied probabilists and statisticians. Readers are expected to have taken a first-year graduate level course in mathematical statistics or in applied probability.