Multistate Models for the Analysis of Life History Data

Author: Richard J Cook,Jerald F. Lawless

Publisher: CRC Press

ISBN: 1351646052

Category: Mathematics

Page: 440

View: 7329

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)

Multistate Analysis of Life Histories with R

Author: Frans Willekens

Publisher: Springer

ISBN: 331908383X

Category: Mathematics

Page: 308

View: 8808

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.

Competing Risks and Multistate Models with R

Author: Jan Beyersmann,Arthur Allignol,Martin Schumacher

Publisher: Springer Science & Business Media

ISBN: 1461420350

Category: Mathematics

Page: 245

View: 4436

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.

Analysis of Multivariate Survival Data

Author: Philip Hougaard

Publisher: Springer Science & Business Media

ISBN: 1461213045

Category: Mathematics

Page: 542

View: 6880

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.

The Statistical Analysis of Recurrent Events

Author: Richard J. Cook,Jerald Lawless

Publisher: Springer Science & Business Media

ISBN: 0387698094

Category: Medical

Page: 404

View: 7537

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the modeling assumptions that they are based on. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies are all covered, with procedures for estimation, testing and model checking.

Survival and Event History Analysis

A Process Point of View

Author: Odd Aalen,Ornulf Borgan,Hakon Gjessing

Publisher: Springer Science & Business Media

ISBN: 038768560X

Category: Mathematics

Page: 540

View: 3262

The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.

Data Analysis with Competing Risks and Intermediate States

Author: Ronald B. Geskus

Publisher: Chapman and Hall/CRC

ISBN: 9781466570351

Category: Mathematics

Page: 277

View: 3673

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 epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. It addresses nonparametric estimation of the relevant quantities. The book then shows how to use a stacked data set that offers great flexibility in the modeling of covariable effects on the transition rates between states. It also describes three ways to quantify effects on the cumulative scale. Each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in SAS and Stata and the functionality in the R program, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. The book’s website provides the R code for the computer practicals along with other material. For researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. Researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.

Multi-State Survival Models for Interval-Censored Data

Author: Ardo Van Den Hout

Publisher: Chapman & Hall

ISBN: 9781466568402

Category: Mathematics

Page: 300

View: 5331

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

Author: Melinda Mills

Publisher: SAGE Publications

ISBN: 1848601026

Category: Social Science

Page: 279

View: 1312

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.

Flowgraph Models for Multistate Time-to-Event Data

Author: Aparna V. Huzurbazar

Publisher: John Wiley & Sons

ISBN: 0471686530

Category: Mathematics

Page: 320

View: 7013

A unique introduction to the innovative methodology of statistical flowgraphs This book offers a practical, application-based approach to flowgraph models for time-to-event data. It clearly shows how this innovative new methodology can be used to analyze data from semi-Markov processes without prior knowledge of stochastic processes--opening the door to interesting applications in survival analysis and reliability as well as stochastic processes. Unlike other books on multistate time-to-event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes: * Clear instructions on how to model multistate time-to-event data using flowgraph models * An emphasis on computation, real data, and Bayesian methods for problem solving * Real-world examples for analyzing data from stochastic processes * The use of flowgraph models to analyze complex stochastic networks * Exercise sets to reinforce the practical approach of this volume Flowgraph Models for Multistate Time-to-Event Data is an invaluable resource/reference for researchers in biostatistics/survival analysis, systems engineering, and in fields that use stochastic processes, including anthropology, biology, psychology, computer science, and engineering.

Dynamic Prediction in Clinical Survival Analysis

Author: Hans van Houwelingen,Hein Putter

Publisher: CRC Press

ISBN: 1439835438

Category: Mathematics

Page: 250

View: 5435

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 after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts: Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated Part III is dedicated to the use of time-dependent information in dynamic prediction Part IV explores dynamic prediction models for survival data using genomic data Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.

Multivariate Survival Analysis and Competing Risks

Author: Martin J. Crowder

Publisher: CRC Press

ISBN: 1439875227

Category: Mathematics

Page: 418

View: 7060

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

Spacecraft Reliability and Multi-State Failures

A Statistical Approach

Author: Joseph Homer Saleh,Jean-François Castet

Publisher: John Wiley & Sons

ISBN: 111995746X

Category: Technology & Engineering

Page: 216

View: 4388

The aerospace community has long recognized and repeatedly emphasizes the importance of reliability for space systems. Despite this, little has been published in book form on the topic. Spacecraft Reliability and Multi-state Failures addresses this gap in the literature, offering a unique focus on spacecraft reliability based on extensive statistical analysis of system and subsystem anomalies and failures. The authors provide new results pertaining to spacecraft reliability based on extensive statistical analysis of on-orbit anomaly and failure data that will be particularly useful to spacecraft manufacturers and designers, for example in guiding satellite (and subsystem) test and screening programs and providing an empirical basis for subsystem redundancy and reliability growth plans. The authors develop nonparametric results and parametric models of spacecraft and spacecraft subsystem reliability and multi-state failures, quantify the relative contribution of each subsystem to the failure of the satellites thus identifying the subsystems that drive spacecraft unreliability, and propose advanced stochastic modeling and analysis tools for the reliability and survivability of spacecraft and space-based networks. Spacecraft Reliability and Multi-state Failures provides new nonparametric results pertaining to spacecraft reliability based on extensive statistical analysis of on-orbit anomaly and failure data; develops parametric models of spacecraft and spacecraft subsystem reliability and multi-state failures quantifies the relative contribution of each subsystem to the failure of the satellites proposes advanced stochastic modeling and analysis tools for the reliability and survivability of spacecraft and space-based networks. provides a dedicated treatment of the reliability and subsystem anomalies of communication spacecraft in geostationary orbit.

The statistical analysis of failure time data

Author: J. D. Kalbfleisch,Ross L. Prentice

Publisher: Wiley-Interscience

ISBN: 9780471055198

Category: Mathematics

Page: 321

View: 6926

Synthesizes statistical models and methods for the analysis of failure time or ''survival'' data. Focuses on regression problems with survival data, specifically the estimation of regression coefficients and distributional shape in the presence of shaping. Deals with the theory, applications and extensions of the proportional hazards model. Includes worked examples and problems for solution.

Flowgraph Models for Multistate Time-to-Event Data

Author: Aparna V. Huzurbazar

Publisher: John Wiley & Sons

ISBN: 0471686530

Category: Mathematics

Page: 320

View: 9498

A unique introduction to the innovative methodology of statistical flowgraphs This book offers a practical, application-based approach to flowgraph models for time-to-event data. It clearly shows how this innovative new methodology can be used to analyze data from semi-Markov processes without prior knowledge of stochastic processes--opening the door to interesting applications in survival analysis and reliability as well as stochastic processes. Unlike other books on multistate time-to-event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes: * Clear instructions on how to model multistate time-to-event data using flowgraph models * An emphasis on computation, real data, and Bayesian methods for problem solving * Real-world examples for analyzing data from stochastic processes * The use of flowgraph models to analyze complex stochastic networks * Exercise sets to reinforce the practical approach of this volume Flowgraph Models for Multistate Time-to-Event Data is an invaluable resource/reference for researchers in biostatistics/survival analysis, systems engineering, and in fields that use stochastic processes, including anthropology, biology, psychology, computer science, and engineering.

Statistical Analysis of Panel Count Data

Author: Jianguo Sun,Xingqiu Zhao

Publisher: Springer Science & Business Media

ISBN: 1461487153

Category: Medical

Page: 271

View: 3341

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.

Analysis of Repeated Measures Data

Author: M. Ataharul Islam,Rafiqul I Chowdhury

Publisher: Springer

ISBN: 9811037949

Category: Business & Economics

Page: 250

View: 2515

This book presents a broad range of statistical techniques to address emerging needs in the field of repeated measures. It also provides a comprehensive overview of extensions of generalized linear models for the bivariate exponential family of distributions, which represent a new development in analysing repeated measures data. The demand for statistical models for correlated outcomes has grown rapidly recently, mainly due to presence of two types of underlying associations: associations between outcomes, and associations between explanatory variables and outcomes. The book systematically addresses key problems arising in the modelling of repeated measures data, bearing in mind those factors that play a major role in estimating the underlying relationships between covariates and outcome variables for correlated outcome data. In addition, it presents new approaches to addressing current challenges in the field of repeated measures and models based on conditional and joint probabilities. Markov models of first and higher orders are used for conditional models in addition to conditional probabilities as a function of covariates. Similarly, joint models are developed using both marginal-conditional probabilities as well as joint probabilities as a function of covariates. In addition to generalized linear models for bivariate outcomes, it highlights extended semi-parametric models for continuous failure time data and their applications in order to include models for a broader range of outcome variables that researchers encounter in various fields. The book further discusses the problem of analysing repeated measures data for failure time in the competing risk framework, which is now taking on an increasingly important role in the field of survival analysis, reliability and actuarial science. Details on how to perform the analyses are included in each chapter and supplemented with newly developed R packages and functions along with SAS codes and macro/IML. It is a valuable resource for researchers, graduate students and other users of statistical techniques for analysing repeated measures data.

Competing Risks and Multistate Models with R

Author: Jan Beyersmann,Arthur Allignol,Martin Schumacher

Publisher: Springer Science & Business Media

ISBN: 1461420350

Category: Mathematics

Page: 245

View: 7484

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.

Bayesian Analysis for Population Ecology

Author: Ruth King,Byron Morgan,Olivier Gimenez,Steve Brooks

Publisher: CRC Press

ISBN: 9781439811887

Category: Mathematics

Page: 456

View: 1140

Novel Statistical Tools for Conserving and Managing Populations By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting. Emphasising model choice and model averaging, Bayesian Analysis for Population Ecology presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book’s website. The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference. Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.

Mathematical Statistics and Probability Theory

Proceedings, Sixth International Conference, Wisła (Poland), 1978

Author: W. Klonecki,A. Kozek,J. Rosinski

Publisher: Springer Science & Business Media

ISBN: 1461573971

Category: Mathematics

Page: 373

View: 2189

Since 1972 the Institute of Mathematics and the Committee of Mathematics of the Polish Academy of Sciences organize annually con ferences on mathematical statistics in Wisla. The 1978 conference, supported also by the University of Wroclaw,was held in Wisla from December 7 to December 13 and attended by around 100 participants from 11 countries. K. Urbanik, Rector of the University of Wroclaw, was the honorary chairman of the conference. Traditionally at these conferences there are presented results on mathematical statistics and related fields obtained in Poland during the year of the conference as well as results presented by invited scholars from other countries. In 1978 invitations to present talks were accepted by 20 e~inent statisticians and probabilists. The topics of the invited lectures and contributed papers included theoretical statistics with a broad cover of the theory of linear models, inferences from stochastic processes, probability theory and applications to biology and medicine. In these notes there appear papers submitted by 30 participants of the conference. During the conference, on December 9, there was held a special session of the Polish Mathematical Society on the occasion of elect ing Professor Jerzy Neyman the honorary member of the Polish Mathematical Society. At this session W. Orlicz, president of the Polish Mathematical Society, K.Krickeberg,president of the Bernoulli Society. R. Bartoszynski and K. Doksum gave talks on Neyman IS con tribution to statistics, his organizational achievements in the U.S.