Statistical Modelling Of Survival Data With Random Effects
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Statistical Modelling Of Survival Data With Random Effects
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Author : Il Do Ha
language : en
Publisher: Springer
Release Date : 2018-01-02
Statistical Modelling Of Survival Data With Random Effects written by Il Do Ha and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-02 with Mathematics categories.
This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.
Statistical Modelling In Biostatistics And Bioinformatics
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Author : Gilbert MacKenzie
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-05-08
Statistical Modelling In Biostatistics And Bioinformatics written by Gilbert MacKenzie 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 2014-05-08 with Mathematics categories.
This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.
Mixed Effects Models For Complex Data
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Author : Lang Wu
language : en
Publisher: CRC Press
Release Date : 2009-11-11
Mixed Effects Models For Complex Data written by Lang Wu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-11 with Mathematics categories.
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors,
Modeling Survival Data Extending The Cox Model
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Author : Terry M. Therneau
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Modeling Survival Data Extending The Cox Model written by Terry M. Therneau 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 2013-11-11 with Mathematics categories.
Extending the Cox Model is aimed at researchers, practitioners, and graduate students who have some exposure to traditional methods of survival analysis. The emphasis is on semiparametric methods based on the proportional hazards model. The inclusion of examples with SAS and S-PLUS code will make the book accessible to most working statisticians.
Statistical Models And Methods For Lifetime Data
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Author : Jerald F. Lawless
language : en
Publisher: John Wiley & Sons
Release Date : 2011-01-25
Statistical Models And Methods For Lifetime Data written by Jerald F. Lawless 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 2011-01-25 with Mathematics categories.
Praise for the First Edition "An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ." -Choice "This is an important book, which will appeal to statisticians working on survival analysis problems." -Biometrics "A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook." -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts. New and expanded coverage in this edition includes: * Observation schemes for lifetime data * Multiple failure modes * Counting process-martingale tools * Both special lifetime data and general optimization software * Mixture models * Treatment of interval-censored and truncated data * Multivariate lifetimes and event history models * Resampling and simulation methodology
Frailty Models In Survival Analysis
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Author : Andreas Wienke
language : en
Publisher:
Release Date : 2024-10-14
Frailty Models In Survival Analysis written by Andreas Wienke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-14 with Mathematics categories.
Accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real data application and interpretation of the results. It extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univa
An Introduction To Survival Analysis Using Stata Revised Edition
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Author : Mario Cleves
language : en
Publisher: Stata Press
Release Date : 2004
An Introduction To Survival Analysis Using Stata Revised Edition written by Mario Cleves and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Business & Economics categories.
Developing the statistical concepts unique to survival data, An Introduction to Survival Analysis Using Stata, Revised Edition includes statistical theory, step-by-step procedures for analyzing survival data, a detailed usage guide for Stata's most widely used st commands, and pointers for using Stata to analyze survival data and present the results. The book covers basic theoretical concepts, censoring and truncation, and the preparation of survival data for analysis using Stata's st analysis commands. It also discusses Cox regression and includes various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, and modeling diagnostics. The final chapters fit parametric models using Stata's streg command.
Multivariate Statistical Modelling Based On Generalized Linear Models
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Author : Ludwig Fahrmeir
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Multivariate Statistical Modelling Based On Generalized Linear Models written by Ludwig Fahrmeir 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 2013-03-14 with Mathematics categories.
Since our first edition of this book, many developments in statistical mod elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.
Bayesian Hierarchical Models
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Author : Peter D. Congdon
language : en
Publisher: CRC Press
Release Date : 2019-09-16
Bayesian Hierarchical Models written by Peter D. Congdon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-16 with Mathematics categories.
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website
Statistical Modelling
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Author : Gilg U.H. Seeber
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Statistical Modelling written by Gilg U.H. Seeber 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 2012-12-06 with Mathematics categories.
This volume presents the published proceedings of the lOth International Workshop on Statistical Modelling, to be held in Innsbruck, Austria from 10 to 14 July, 1995. This workshop marks an important anniversary. The inaugural workshop in this series also took place in Innsbruck in 1986, and brought together a small but enthusiastic group of thirty European statisticians interested in statistical modelling. The workshop arose out of two G LIM conferences in the U. K. in London (1982) and Lancaster (1985), and from a num ber of short courses organised by Murray Aitkin and held at Lancaster in the early 1980s, which attracted many European statisticians interested in Generalised Linear Modelling. The inaugural workshop in Innsbruck con centrated on GLMs and was characterised by a number of features - a friendly and supportive academic atmosphere, tutorial sessions and invited speakers presenting new developments in statistical modelling, and a very well organised social programme. The academic programme allowed plenty of time for presentation and for discussion, and made available copies of all papers beforehand. Over the intervening years, the workshop has grown substantially, and now regularly attracts over 150 participants. The scope of the workshop is now much broader, reflecting the growth in the subject of statistical modelling over ten years. The elements ofthe first workshop, however, are still present, and participants always find the meetings relevant and stimulating.