Multistate Analysis of Life Histories with R PDF Download

Author: Frans Willekens
Publisher: Springer
ISBN: 331908383X
Category : Mathematics
Languages : en
Pages : 323

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Book Description
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.

Author: Odd Aalen
Publisher: Springer Science & Business Media
ISBN: 038768560X
Category : Mathematics
Languages : en
Pages : 550

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Book Description
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.

Author: Andreas Diekmann
Publisher: Academic Press
ISBN: 1483266567
Category : Mathematics
Languages : en
Pages : 352

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Book Description
Stochastic Modelling of Social Processes provides information pertinent to the development in the field of stochastic modeling and its applications in the social sciences. This book demonstrates that stochastic models can fulfill the goals of explanation and prediction. Organized into nine chapters, this book begins with an overview of stochastic models that fulfill normative, predictive, and structural–analytic roles with the aid of the theory of probability. This text then examines the study of labor market structures using analysis of job and career mobility, which is one of the approaches taken by sociologists in research on the labor market. Other chapters consider the characteristic trends and patterns from data on divorces. This book discusses as well the two approaches of stochastic modeling of social processes, namely competing risk models and semi-Markov processes. The final chapter deals with the practical application of regression models of survival data. This book is a valuable resource for social scientists and statisticians.

Author: Ardo van den Hout
Publisher: CRC Press
ISBN: 1315356732
Category : Mathematics
Languages : en
Pages : 323

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Book Description
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.

Author: Melinda Mills
Publisher: SAGE
ISBN: 1848601026
Category : Social Science
Languages : en
Pages : 301

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Book Description
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.

Author: Kazuo Yamaguchi
Publisher: SAGE
ISBN: 9780803933248
Category : Medical
Languages : en
Pages : 200

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Book Description
"In a manner similar to many other titles within the Applied Social Research Methods Series, this 182-page book thoroughly covers many of the specific methodological hurdles encountered in implementing event history analysis (EHA). The Applied Social Research Methods Series' ... is the result of careful subject selection. ... Consistent with the practical orientation of the book, each of the application sections provides useful insights into data structure problems and programming notes. ... Kazuo Yamaguchi's insightful review of problems in structuring EHA models is useful for those contemplating life-course research. ... We strongly recommend its inclusion in the libraries of marketing researchers and its inclusion on suggested reading lists of graduate research method seminars."--Journal of Marketing Research "This book, which is part of Sage Publications' Applied Social Research Methods Series, is a practical guide for those interested in using event history analysis. ... The book's strength is that it is well written and easy to understand. Even those with limited statistical backgrounds can follow the discussion and the systematic progression from the simpler to the more complex models (although the author provides ample references for those wanting a more rigorous discussion). ... Upon finishing the book, I found myself wondering about specific accounting questions that might be addressed using event history analysis. There are many, and in fact, most issues can be recast in an events framework. ... In sum, I recommend this book to anyone wanting to use event history analysis whether to apply to new research questions or to provide a fresh look at old questions." --The Accounting Review "A significant introduction to the event-history literature that provides the background to implement this difficult methodology successfully and that can be supplemented with other, more advanced texts. It will undoubtedly become a prized text among students and a valuable reference for the research community." --Contemporary Sociology As a research tool event history analysis has recently become a key technique for researchers, professionals and students in a wide range of disciplines. However, despite this increasing interest, few resources exist which clearly examine this technique. Now, Event History Analysis provides a systematic introduction to models, methods and applications of event history analysis. Kazuo Yamaguchi emphasizes "hands on" information, including the use and misuse of samples, models, and covariates in applications, the structural arrangement of input data, the specification of various models in such computer programs as SAS-LOGIST and SPSS-LOGLINEAR, and the interpretation of parameters estimated from models. This timely book also offers such significant topics as missing data, hazard rate, Cox's partial likelihood model, survivor function, and discrete-time logit models for both one-way and two-way transitions. Event History Analysis is essential for researchers, professionals and students of public health, sociology, labor economics, political science, and organization studies.-Provided by published.

Author: Hal Caswell
Publisher: Springer
ISBN: 3030105342
Category : Social Science
Languages : en
Pages : 308

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Book Description
This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.

Author: Juha Alho
Publisher: Springer Science & Business Media
ISBN: 0387283927
Category : Social Science
Languages : en
Pages : 432

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Book Description
Provides a unique introduction to demographic problems in a familiar language. Presents a unified statistical outlook on both classical methods of demography and recent developments. Exercises are included to facilitate its classroom use. Both authors have contributed extensively to statistical demography and served in advisory roles and as statistical consultants in the field.

Author: Philip Hougaard
Publisher: Springer Science & Business Media
ISBN: 1461213045
Category : Mathematics
Languages : en
Pages : 559

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Book Description
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.

Author: Colin O. Wu
Publisher: CRC Press
ISBN: 0429939078
Category : Mathematics
Languages : en
Pages : 512

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Book Description
Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features: • Provides an overview of parametric and semiparametric methods • Shows smoothing methods for unstructured nonparametric models • Covers structured nonparametric models with time-varying coefficients • Discusses nonparametric shared-parameter and mixed-effects models • Presents nonparametric models for conditional distributions and functionals • Illustrates implementations using R software packages • Includes datasets and code in the authors’ website • Contains asymptotic results and theoretical derivations