The Experts below are selected from a list of 107430 Experts worldwide ranked by ideXlab platform
Stacia M. Desantis - One of the best experts on this subject based on the ideXlab platform.
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Bayesian quantile regression joint models: Inference and dynamic predictions.
Statistical Methods in Medical Research, 2018Co-Authors: Ming Yang, Stacia M. DesantisAbstract:: In the traditional joint models of a Longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the Longitudinal Process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the Longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the Longitudinal Process. To this end, quantile regression provides a flexible, distribution-free way to study covariate effects at different quantiles of the Longitudinal outcome and it is robust not only to deviation from normality, but also to outlying observations. In this article, we present and advocate the linear quantile mixed model for the Longitudinal Process in the joint models framework. Our development is motivated by a large prospective study of Huntington's disease where primary clinical interest is in utilizing Longitudinal motor scores and other early covariates to predict the risk of developing Huntington's disease. We develop a Bayesian method based on the location-scale representation of the asymmetric Laplace distribution, assess its performance through an extensive simulation study, and demonstrate how this linear quantile mixed model-based joint models approach can be used for making subject-specific dynamic predictions of survival probability.
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Bayesian quantile regression joint models: Inference and dynamic predictions.
Statistical methods in medical research, 2018Co-Authors: Ming Yang, Sheng Luo, Stacia M. DesantisAbstract:In the traditional joint models of a Longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the Longitudinal Process. However, in many circumsta...
Pulak Ghosh - One of the best experts on this subject based on the ideXlab platform.
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a bayesian semiparametric multivariate joint model for multiple Longitudinal outcomes and a time to event
Statistics in Medicine, 2011Co-Authors: Dimitris Rizopoulos, Pulak GhoshAbstract:Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple Longitudinal outcomes to a time-to-event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject-specific Longitudinal evolutions we use a spline-based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the Longitudinal Process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them. Copyright © 2011 John Wiley & Sons, Ltd.
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A Bayesian semiparametric multivariate joint model for multiple Longitudinal outcomes and a time-to-event.
Statistics in medicine, 2011Co-Authors: Dimitris Rizopoulos, Pulak GhoshAbstract:Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple Longitudinal outcomes to a time-to-event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject-specific Longitudinal evolutions we use a spline-based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the Longitudinal Process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them.
Dimitris Rizopoulos - One of the best experts on this subject based on the ideXlab platform.
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a bayesian semiparametric multivariate joint model for multiple Longitudinal outcomes and a time to event
Statistics in Medicine, 2011Co-Authors: Dimitris Rizopoulos, Pulak GhoshAbstract:Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple Longitudinal outcomes to a time-to-event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject-specific Longitudinal evolutions we use a spline-based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the Longitudinal Process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them. Copyright © 2011 John Wiley & Sons, Ltd.
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A Bayesian semiparametric multivariate joint model for multiple Longitudinal outcomes and a time-to-event.
Statistics in medicine, 2011Co-Authors: Dimitris Rizopoulos, Pulak GhoshAbstract:Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple Longitudinal outcomes to a time-to-event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject-specific Longitudinal evolutions we use a spline-based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the Longitudinal Process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them.
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Nonignorable models for intermittently missing categorical Longitudinal responses.
Biometrics, 2009Co-Authors: Roula Tsonaka, Dimitris Rizopoulos, Geert Verbeke, Emmanuel LesaffreAbstract:A class of nonignorable models is presented for handling nonmonotone missingness in categorical Longitudinal responses. This class of models includes the traditional selection models and shared parameter models. This allows us to perform a broader than usual sensitivity analysis. In particular, instead of considering variations to a chosen nonignorable model, we study sensitivity between different missing data frameworks. An appealing feature of the developed class is that parameters with a marginal interpretation are obtained, while algebraically simple models are considered. Specifically, marginalized mixed-effects models (Heagerty, 1999, Biometrics 55, 688-698) are used for the Longitudinal Process that model separately the marginal mean and the correlation structure. For the correlation structure, random effects are introduced and their distribution is modeled either parametrically or non-parametrically to avoid potential misspecifications.
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A two-part joint model for the analysis of survival and Longitudinal binary data with excess zeros.
Biometrics, 2007Co-Authors: Dimitris Rizopoulos, Geert Verbeke, Emmanuel Lesaffre, Yves VanrenterghemAbstract:Many Longitudinal studies generate both the time to some event of interest and repeated measures data. This article is motivated by a study on patients with a renal allograft, in which interest lies in the association between Longitudinal proteinuria (a dichotomous variable) measurements and the time to renal graft failure. An interesting feature of the sample at hand is that nearly half of the patients were never tested positive for proteinuria (>/=1g/day) during follow-up, which introduces a degenerate part in the random-effects density for the Longitudinal Process. In this article we propose a two-part shared parameter model framework that effectively takes this feature into account, and we investigate sensitivity to the various dependence structures used to describe the association between the Longitudinal measurements of proteinuria and the time to renal graft failure.
Ming Yang - One of the best experts on this subject based on the ideXlab platform.
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Bayesian quantile regression joint models: Inference and dynamic predictions.
Statistical Methods in Medical Research, 2018Co-Authors: Ming Yang, Stacia M. DesantisAbstract:: In the traditional joint models of a Longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the Longitudinal Process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the Longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the Longitudinal Process. To this end, quantile regression provides a flexible, distribution-free way to study covariate effects at different quantiles of the Longitudinal outcome and it is robust not only to deviation from normality, but also to outlying observations. In this article, we present and advocate the linear quantile mixed model for the Longitudinal Process in the joint models framework. Our development is motivated by a large prospective study of Huntington's disease where primary clinical interest is in utilizing Longitudinal motor scores and other early covariates to predict the risk of developing Huntington's disease. We develop a Bayesian method based on the location-scale representation of the asymmetric Laplace distribution, assess its performance through an extensive simulation study, and demonstrate how this linear quantile mixed model-based joint models approach can be used for making subject-specific dynamic predictions of survival probability.
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Bayesian quantile regression joint models: Inference and dynamic predictions.
Statistical methods in medical research, 2018Co-Authors: Ming Yang, Sheng Luo, Stacia M. DesantisAbstract:In the traditional joint models of a Longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the Longitudinal Process. However, in many circumsta...
Cecile Proustlima - One of the best experts on this subject based on the ideXlab platform.
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joint modelling of Longitudinal and multi state Processes application to clinical progressions in prostate cancer
Statistics in Medicine, 2016Co-Authors: Loic Ferrer, James J Dignam, Tom Pickles, Helene Jacqmingadda, Virginie Rondeau, Cecile ProustlimaAbstract:Joint modelling of Longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link between Longitudinal measures of prostate-specific antigen (PSA) and time of clinical recurrence when studying the risk of relapse. In practice, multiple types of relapse may occur successively. Distinguishing these transitions between health states would allow to evaluate, for example, how PSA trajectory and classical covariates impact the risk of dying after a distant recurrence post-radiotherapy, or to predict the risk of one specific type of clinical recurrence post-radiotherapy, from the PSA history. In this context, we present a joint model for a Longitudinal Process and a multi-state Process, which is divided into two sub-models: a linear mixed sub-model for Longitudinal data and a multi-state sub-model with proportional hazards for transition times, both linked by a function of shared random effects. Parameters of this joint multi-state model are estimated within the maximum likelihood framework using an EM algorithm coupled with a quasi-Newton algorithm in case of slow convergence. It is implemented under R, by combining and extending mstate and JM packages. The estimation program is validated by simulations and applied on pooled data from two cohorts of men with localized prostate cancer. Thanks to the classical covariates available at baseline and the repeated PSA measurements, we are able to assess the biomarker's trajectory, define the risks of transitions between health states and quantify the impact of the PSA dynamics on each transition intensity. Copyright © 2016 John Wiley & Sons, Ltd.
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joint modelling of Longitudinal and multi state Processes application to clinical progressions in prostate cancer
arXiv: Applications, 2015Co-Authors: Loic Ferrer, James J Dignam, Tom Pickles, Helene Jacqmingadda, Virginie Rondeau, Cecile ProustlimaAbstract:Joint modelling of Longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link between Longitudinal measures of prostate-specific antigen (PSA) and the time of clinical recurrence when studying the risk of relapse. In practice, multiple types of relapse may occur successively. Distinguishing these transitions between health states would allow to evaluate, for example, how PSA trajectory and classical covariates impact the risk of dying after a distant recurrence post-radiotherapy, or to predict the risk of one specific type of clinical recurrence post-radiotherapy, from the PSA history. In this context, we present a joint model for a Longitudinal Process and a multi-state Process which is divided into two sub-models: a linear mixed sub-model for Longitudinal data, and a multi-state sub-model with proportional hazards for transition times, both linked by shared random effects. Parameters of this joint multi-state model are estimated within the maximum likelihood framework using an EM algorithm coupled to a quasi-Newton algorithm in case of slow convergence. It is implemented under R, by combining and extending the mstate and JM packages. The estimation program is validated by simulations and applied on pooled data from two cohorts of men with localized prostate cancer and treated by radiotherapy. Thanks to the classical covariates available at baseline and the PSA measurements collected repeatedly during the follow-up, we are able to assess the biomarker's trajectory, define the risks of transitions between health states, and quantify the impact of the PSA dynamics on each transition intensity.
