Competing Risks and Multistate Models with R

Competing Risks and Multistate Models with R

This book covers competing risks and multistate models, sometimes summarized as event history analysis.

Author: Jan Beyersmann

Publisher: Springer Science & Business Media

ISBN: 9781461420354

Category: Mathematics

Page: 245

View: 121

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.
Categories: Mathematics

Textbook of Clinical Trials in Oncology

Textbook of Clinical Trials in Oncology

Beyersmann J, Allignol A, Schumacher M. Competing Risks and Multistate
Models with R, Use R!, First Edition. New York: Springer-Verlag, 2012. 8.
Andersen PK. Decomposition of number of life years lost according to causes of
death.

Author: Susan Halabi

Publisher: CRC Press

ISBN: 9781351620963

Category: Medical

Page: 626

View: 252

There is an increasing need for educational resources for statisticians and investigators. Reflecting this, the goal of this book is to provide readers with a sound foundation in the statistical design, conduct, and analysis of clinical trials. Furthermore, it is intended as a guide for statisticians and investigators with minimal clinical trial experience who are interested in pursuing a career in this area. The advancement in genetic and molecular technologies have revolutionized drug development. In recent years, clinical trials have become increasingly sophisticated as they incorporate genomic studies, and efficient designs (such as basket and umbrella trials) have permeated the field. This book offers the requisite background and expert guidance for the innovative statistical design and analysis of clinical trials in oncology. Key Features: Cutting-edge topics with appropriate technical background Built around case studies which give the work a "hands-on" approach Real examples of flaws in previously reported clinical trials and how to avoid them Access to statistical code on the book’s website Chapters written by internationally recognized statisticians from academia and pharmaceutical companies Carefully edited to ensure consistency in style, level, and approach Topics covered include innovating phase I and II designs, trials in immune-oncology and rare diseases, among many others
Categories: Medical

Multistate Analysis of Life Histories with R

Multistate Analysis of Life Histories with R

Nelson-Aalen estimator of the cumulative hazard in multistate models. ... R
Newsletter, 8(2), 48–50. ... Empirical transition matrix of multistate models: The
etm package. ... Competing risks and multistate models with R. New York:
Springer. ... Springer International Publishing Switzerland 2014 F. Willekens,
Multistate Analysis of Life Histories with R, Use R!, DOI 10.1007/978-3-319-
08383-4 299 Borgan, ...

Author: Frans Willekens

Publisher: Springer

ISBN: 9783319083834

Category: Mathematics

Page: 308

View: 602

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.
Categories: Mathematics

Multistate Models for the Analysis of Life History Data

Multistate Models for the Analysis of Life History Data

The use of propensity score methods with survival or time-toevent outcomes:
reporting measures of effect similar to those used in ... Competing Risks and
Multistate Models with R. Springer Science + Business Media, New York, NY.
Binder, N.

Author: Richard J Cook

Publisher: CRC Press

ISBN: 9781498715614

Category: Mathematics

Page: 440

View: 99

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. 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 Richard Cook is Canada Research Chair in Statistical Methods for Health Research at the University of Waterloo. He has received the Gold Medal of the Statistical Society of Canada and is a Fellow of the American Statistical Association. He collaborates and consults widely on health research and has given many short courses. He and Dr. Lawless previously coauthored the influential book, The Statistical Analysis of Recurrent Events (Springer, 2007). Jerald Lawless is Distinguished Professor Emeritus at the University of Waterloo. He is a Fellow of the Royal Society of Canada, a Gold Medal recipient of the Statistical Society of Canada and Fellow of the American Statistical Association. He is a past editor of Technometrics and has collaborated and consulted in numerous areas. He has presented many short courses, with Dr. Cook and individually. "The authors of the book are internationally renowned experts in the field of multi-state modeling and have written an extremely clear and comprehensive book on the topic that covers many different aspects, from the fundamental theory to the practical side of analyzing data and interpreting results. The examples are well chosen to represent the most common types of multi-state processes that public health researchers could encounter. The inclusion of software code to illustrate how the models can be fit and interpreted is especially helpful to readers." (Mimi Kim, Albert Einstein College of Medicine)
Categories: Mathematics

Data Analysis with Competing Risks and Intermediate States

Data Analysis with Competing Risks and Intermediate States

An overview of packages for competing risks analysis in R is given in the section
Multistate Models” of the CRAN Task ... can use standard software to estimate
the subdistribution hazard by redefining the risk set: an individual is in follow-up ...

Author: Ronald B. Geskus

Publisher: CRC Press

ISBN: 9781466570368

Category: Mathematics

Page: 277

View: 596

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
Categories: Mathematics

Regression with Linear Predictors

Regression with Linear Predictors

Andersen PK, Abildstrom SZ, RosthøjS (2002b) Competing risks as a multistate
model. ... Blackwell, Malden, MA Begg CB, Gray R (1984) Calculation of
polychotomous logistic regression parameters using individualized regressions.

Author: Per Kragh Andersen

Publisher: Springer

ISBN: 144197170X

Category: Mathematics

Page: 494

View: 865

This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression,- gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear,logistic,proportional hazards,andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor:thatis,t-tests,one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2×2, 2×(k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model “held ?xed”.
Categories: Mathematics

The Statistical Analysis of Failure Time Data

The Statistical Analysis of Failure Time Data

Addressing graduate students, practitioners, and researchers, Jack Kalbfleisch and Ross Prentice update their classic text with these and other current developments in the second edition of The Statistical Analysis of Failure Time Data.

Author: John D. Kalbfleisch

Publisher: John Wiley & Sons

ISBN: 9781118031230

Category: Mathematics

Page: 462

View: 485

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.
Categories: Mathematics

Handbook of Survival Analysis

Handbook of Survival Analysis

With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches.

Author: John P. Klein

Publisher: CRC Press

ISBN: 9781466555679

Category: Mathematics

Page: 656

View: 280

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
Categories: Mathematics

Annual Review of Sociology

Annual Review of Sociology

The basic life table is a simple type of event history model as it analyzes mortality
events whose risk varies with the ... bring the power of regression analysis to
multistate models by estimating hazard rates ( or risks of transfer ) using many
more variables ... De - mography 21 : 71 - 82 Bumpass , L . L . , Rindfuss , R . R .
1979 . ... A stochastic model of competing risks of illness and competing risks of
death .

Author: W. Richard Scott

Publisher:

ISBN: 0824322142

Category: Social Science

Page: 545

View: 728

Categories: Social Science

The American Statistician

The American Statistician

Andersen , P . , Abildstrom , S . , and Rosthoj , S . ( 2002 ) , “ Competing Risks as
a Multi - state Model ... Gill , R . D . , and Johansen , S . ( 1990 ) , “ A Survey of
Product - Integration with a View Towards Application in Survival Analysis , ” The
 ...

Author:

Publisher:

ISBN: UCSC:32106018842960

Category: Statistics

Page:

View: 977

Categories: Statistics

Proceedings of the Biometrics Section

Proceedings of the Biometrics Section

Estimation of the Association between Bivariate Failure Times in Semi -
Competing Risk Problem Hongyu Jiang ... A numerical If only the time and cause
of the first event are diagnostic is described for checking model over the recorded
, that is , Ny and R are disregarded , then the support of the ... Consider two
independent be viewed as a multi - state process and the transi pairs ( X , Y ; )
and ( X ; , Y ; ) .

Author: American Statistical Association. Biometrics Section

Publisher:

ISBN: UOM:39015053988096

Category: Biometry

Page:

View: 700

Categories: Biometry

Biometrics

Biometrics

Onset dulilu ) - To Due to disease 1 , ( u ) dialt | u ) o Competing risks i 22 ( u ) O
Competing risks Figure 1 . A multistate model . The procedures ... An illness -
death model for the study of the carcinogenic process using survival / sacrifice
data .

Author:

Publisher:

ISBN: UCAL:B5031648

Category: Biometry

Page:

View: 405

Categories: Biometry

Encyclopedia of biostatistics

Encyclopedia of biostatistics

alternative models with other relative risk functions r(p"Z() may be used. ...
Multistate Models Models for survival data may be considered a special case of a
multistate model; namely, a model with a ... data and that of more general models
like the competing risks model and the illness-death model to be discussed
below. ... at time 0, is simply the survival probability S(t) which, as described
above, may be estimated using the Kaplan-Meier estimator, which is the product-
integral of (12).

Author: Peter Armitage

Publisher:

ISBN: 0471975761

Category: Mathematics

Page: 690

View: 625

Categories: Mathematics

Biostatistical Genetics and Genetic Epidemiology

Biostatistical Genetics and Genetic Epidemiology

Robert C. Elston, Jane M. Olson, Lyle Palmer ... 1 ) ) Grambsch & Therneau ( 35 )
have proposed plotting a smooth of B + V ( t ) - ' r * ( t ) against t in order to get a
feel of ß ( t ) . ... They may represent competing risks or more generally the
various states in a multistate model . In the classic heart transplant situation , for
example , one might use Cox regression to model the transition from
identification as a ...

Author: Robert C. Elston

Publisher: Wiley-Blackwell

ISBN: UOM:39015054401784

Category: Mathematics

Page: 831

View: 257

This is the third in a series of books based on articles from the Encyclopedia of Biostatistics. The editors have updated the articles from the Human Genetics section of the EoB, have adpated other articles to give them a genetic feel, and have included a number of newly commissioned articles.
Categories: Mathematics

Thomas Hematopoietic Cell Transplantation

Thomas  Hematopoietic Cell Transplantation

E. Donnall Thomas Frederick R. Appelbaum, Stephen J. Forman, Karl G. Blume,
Robert S. Negrin. 45. ... donor lymphocyte infusion therapy for patients with
leukemia who relapse after allografting : application of a multistate model . ...
Pintilie M. Dealing with competing risks : testing covariates and calculating
sample size .

Author: E. Donnall Thomas

Publisher: Wiley-Blackwell

ISBN: NWU:35558005721242

Category: Medical

Page: 1718

View: 640

Accompanying CD-ROM contains ... "full content of the book, database of figures, search function."--CD-ROM label.
Categories: Medical

Proceedings of the Statistical Computing Section

Proceedings of the Statistical Computing Section

Figure 1 : A progressive model. induces a competing risks situation. ... for
example: • aki{t;9) = ah0{t;9) i.e. same a for all individuals . ahi(t;e,Zi(t)) = aM((;7)r(
4(t)^) i.e. multiplicative hazard regression . aw(t; 9, ... environment for event
history analysis should allow the user easily to specify, fit, and diagose multistate
models.

Author: American Statistical Association. Statistical Computing Section

Publisher:

ISBN: UOM:35128001654662

Category: Mathematical statistics

Page:

View: 138

Categories: Mathematical statistics

Wiley Encyclopedia of Clinical Trials

Wiley Encyclopedia of Clinical Trials

Let us finally remark that while , following Hald [ 9 , 10 , p . 144 ) ... A
nonidentifiability aspect of the problem of competing risks , Proceedings of the
National Academy of Sciences 72 , 20 - 22 . 16 . ... Competing risks as a
multistate model .

Author: Lisa Marie Sullivan

Publisher: Wiley-Interscience

ISBN: UCLA:L0099757734

Category: Reference

Page: 4800

View: 739

Here you'll find more than 500 entries from the world's leading experts in the field on the basic concepts, methodologies, and applications in clinical trials. The range of topics includes: basic statistical concepts, design and analysis of clinical trials, ethics, regulatory issues, and methodologies for clinical data management and analysis
Categories: Reference

Mortality Burden of Disease and Injury

Mortality Burden of Disease and Injury

... initial chapter I review the basic concepts of measuring burden of disease and
injury using a simple multistate model . ... I show how an epidemiologist can use
a very simple multistate model to derive general equations for a competing risk ...
Using the powerful and versatile R programming language , in Chapter 5 ( p .

Author: Tomas Javier Aragon

Publisher:

ISBN: UCAL:C3445431

Category:

Page: 316

View: 251

Categories: