Current Methods for Recurrent Events Data With Dependent Termination A Bayesian Perspective
There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid future users in understanding the implications of modeling assumptions and modeling properties, we review the state-...
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| Vydané v: | Journal of the American Statistical Association Ročník 103; číslo 482; s. 866 - 878 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Alexandria, VA
Taylor & Francis
01.06.2008
American Statistical Association Taylor & Francis Ltd |
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| ISSN: | 0162-1459, 1537-274X |
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| Abstract | There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid future users in understanding the implications of modeling assumptions and modeling properties, we review the state-of-the-art statistical methods and present novel theoretical properties, identifiability results, and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with 2 noninformative termination process, we focus on a class of models that allows both negative and positive association between the risk of termination and the rate of recurrent events through a frailty variable. We also discuss the relationship, as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of the current methodology through an analysis of a data set from a clinical trial. Finally, we explore possible future extensions and limitations of the methodology. |
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| AbstractList | There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid future users in understanding the implications of modeling assumptions and modeling properties, we review the state-of-the-art statistical methods and present novel theoretical properties, identifiability results, and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with 2 noninformative termination process, we focus on a class of models that allows both negative and positive association between the risk of termination and the rate of recurrent events through a frailty variable. We also discuss the relationship, as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of the current methodology through an analysis of a data set from a clinical trial. Finally, we explore possible future extensions and limitations of the methodology. [PUBLICATION ABSTRACT] There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid future users in understanding the implications of modeling assumptions and modeling properties, we review the state-of-the-art statistical methods and present novel theoretical properties, identifiability results, and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with 2 noninformative termination process, we focus on a class of models that allows both negative and positive association between the risk of termination and the rate of recurrent events through a frailty variable. We also discuss the relationship, as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of the current methodology through an analysis of a data set from a clinical trial. Finally, we explore possible future extensions and limitations of the methodology. There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid the future users in understanding the implications of modeling assumptions and modeling properties, we review the state of the art statistical methods and present novel theoretical properties, identifiability results and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with noninformative termination process, we focus on a class of models which allows both negative and positive association between the risk of termination and the rate of recurrent events via a frailty variable. We also discuss the relationship as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of current methodology through an analysis of a data set from a clinical trial. In conclusion, we explore possible future extensions and limitations of the methodology. There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid the future users in understanding the implications of modeling assumptions and modeling properties, we review the state of the art statistical methods and present novel theoretical properties, identifiability results and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with noninformative termination process, we focus on a class of models which allows both negative and positive association between the risk of termination and the rate of recurrent events via a frailty variable. We also discuss the relationship as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of current methodology through an analysis of a data set from a clinical trial. In conclusion, we explore possible future extensions and limitations of the methodology.There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid the future users in understanding the implications of modeling assumptions and modeling properties, we review the state of the art statistical methods and present novel theoretical properties, identifiability results and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with noninformative termination process, we focus on a class of models which allows both negative and positive association between the risk of termination and the rate of recurrent events via a frailty variable. We also discuss the relationship as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of current methodology through an analysis of a data set from a clinical trial. In conclusion, we explore possible future extensions and limitations of the methodology. |
| Author | Sinha, Debajyoti Ibrahim, Joseph G Ouyang, Bichun Maiti, Tapabrata |
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| Keywords | Biometrics Bayes estimation Stochastic model Monte Carlo method Data analysis Identifiability Semiparametric Bayes Statistical distribution Mixed Poisson process Statistical association Statistical estimation Multivariate analysis Stochastic method Gibbs sampling M-site model Markov chain Statistical method Medical science Numerical analysis Risk rate Clinical trial Frailty Approximation theory Application |
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| SubjectTerms | Algorithms Applications Bayesian analysis Censorship Clinical research Clinical trials Data Estimation Exact sciences and technology Frailty General topics Gibbs sampler Gibbs sampling Global analysis, analysis on manifolds Graft rejection Health care costs Inference Logical givens M-site model Markov analysis Mathematics Medical sciences Mixed Poisson process Modeling Multivariate analysis Musical intervals Novels Parametric models Probability and statistics Property Recurrent Recurrent events Research methods Review Article Sciences and techniques of general use Semiparametric Bayes Statistical analysis Statistical methods Statistics Stochastic models Stochastic processes Termination Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds Usefulness |
| Subtitle | A Bayesian Perspective |
| Title | Current Methods for Recurrent Events Data With Dependent Termination |
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