检索结果-内蒙古大学图书馆

Population parametrization of costly black box models using iterations between saem algorithm and kriging

COMPUTATIONAL & APPLIED MATHEMATICS 2018年第1期37卷 161-173页

作者： Grenier, Emmanuel Helbert, Celine Louvet, Violaine Samson, Adeline Vigneaux, Paul Univ Lyon CNRS UMR 5669 ENS Lyon UMPA F-69364 Lyon France Univ Lyon CNRS UMR 5208 Ecole Cent Lyon ICJ F-69622 Villeurbanne France Univ Grenoble Alpes GRICAD Grenoble France Univ Grenoble Alpes LJK F-38000 Grenoble France CNRS LJK F-38000 Grenoble France INRIA Grenoble Rhone Alpes Numed Team F-69364 Lyon France

In this article we focus on parametrization of black box models from repeated measurements among several individuals (population parametrization). We introduce a variant of the saem algorithm, called Ksaem algorithm, which couples the standard saem algorithm with the dynamic construction of an approximate metamodel. The costly evaluation of the genuine black box is replaced by a kriging step, using a basis of precomputed values, basis which is enlarged during saem algorithm to improve the accuracy of the metamodel in regions of interest.

关键词： Parameters estimation saem algorithm Kriging Non-linear mixed effect models Partial differential equations KPP equation

来源：评论

学校读者我要写书评

暂无评论

Using the saem algorithm for Mechanistic Joint Models Characterizing the Relationship between Nonlinear PSA Kinetics and Survival in Prostate Cancer Patients

引用

BIOMETRICS 2017年第1期73卷 305-312页

作者： Desmee, Solene Mentre, France Veyrat-Follet, Christine Sebastien, Bernard Guedj, Jeremie INSERM IAME UMR 1137 F-75018 Paris France Universit Paris Diderot IAME UMR 1137 Sorbonne Paris Cite F-75018 Paris France Sanofi Drug Disposit Disposit Safety & Anim Res Dept Alfortville France Sanofi Biostat & Programming Chilly Mazarin France

Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.

关键词： Joint model Metastatic prostate cancer Nonlinear mixed effect model Prostate specific antigen saem algorithm Survival

来源：评论

学校读者我要写书评

暂无评论

A saem algorithm for fused lasso penalized Non Linear Mixed Effect Models: Application to group comparison in pharmacokinetics

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2016年 95卷 207-221页

作者： Oilier, Edouard Samson, Adeline Delavenne, Xavier Viallon, Vivian Ecole Normale Super Lyon CNRS UMR 5669 UMPA Lyon France INRIA Project Team NUMED F-69364 Lyon 07 France Univ Grenoble Alpes LJK F-38000 Grenoble France CNRS LJK F-38000 Grenoble France Univ St Etienne EA3065 Grp Rech Thrombose F-42023 St Etienne France Univ Lyon F-69622 Lyon France Univ Lyon 1 UMRESTTE F-69373 Lyon France IFSTTAR UMRESTTE F-69675 Bron France

Nonlinear mixed effect models are classical tools to analyze nonlinear longitudinal data in many fields such as population pharmacokinetics. Groups of observations are usually compared by introducing the group affiliations as binary covariates with a reference group that is stated among the groups. This approach is relatively limited as it allows only the comparison of the reference group to the others. The proposed method compares groups using a penalized likelihood approach. Groups are described by the same structural model but with parameters that are group specific. The likelihood is penalized with a fused lasso penalty that induces sparsity in the differences between groups for both fixed effects and variances of random effects. A penalized Stochastic Approximation EM algorithm is proposed that is coupled to Alternating Direction Method Multipliers to solve the maximization step. An extensive simulation study illustrates the performance of this algorithm when comparing more than two groups. Then the approach is applied to real data from two pharmacokinetic drug-drug interaction trials. (c) 2015 Elsevier B.V. All rights reserved.

关键词： Nonlinear mixed effect model saem algorithm Fused lasso Group comparison Pharmacokinetics

来源：评论

学校读者我要写书评

暂无评论

Maximum likelihood estimation in discrete mixed hidden Markov models using the saem algorithm

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第6期56卷 2073-2085页

作者： Delattre, M. Lavielle, M. INRIA Saclay Ile France F-91405 Orsay France Univ Paris 11 F-91405 Orsay France

Mixed hidden Markov models have been recently defined in the literature as an extension of hidden Markov models for dealing with population studies. The notion of mixed hidden Markov models is particularly relevant for modeling longitudinal data collected during clinical trials, especially when distinct disease stages can be considered. However, parameter estimation in such models is complex, especially due to their highly nonlinear structure and the presence of unobserved states. Moreover, existing inference algorithms are extremely time consuming when the model includes several random effects. New inference procedures are proposed for estimating population parameters, individual parameters and sequences of hidden states in mixed hidden Markov models. The main contribution consists of a specific version of the stochastic approximation EM algorithm coupled with the Baum-Welch algorithm for estimating population parameters. The properties of this algorithm are investigated via a Monte-Carlo simulation study, and an application of mixed hidden Markov models to the description of daily seizure counts in epileptic patients is presented. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Nonlinear mixed effects model saem algorithm Forward backward algorithm Epileptic seizures count

来源：评论

学校读者我要写书评

暂无评论

Estimation in Truncated GLG Model for Ordered Categorical Spatial Data Using the saem algorithm

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2014年第3期43卷 528-537页

作者： Kaveh, Marjan Khaledi, Majid Jafari Tarbiat Modares Univ Dept Stat Tehran Iran Inst Res Fundamental Sci IPM Sch Math Tehran Iran

In this article, we utilize a scale mixture of Gaussian random field as a tool for modeling spatial ordered categorical data with non-Gaussian latent variables. In fact, we assume a categorical random field is created by truncating a Gaussian Log-Gaussian latent variable model to accommodate heavy tails. Since the traditional likelihood approach for the considered model involves high-dimensional integrations which are computationally intensive, the maximum likelihood estimates are obtained using a stochastic approximation expectation-maximization algorithm. For this purpose, Markov chain Monte Carlo methods are employed to draw from the posterior distribution of latent variables. A numerical example illustrates the methodology.

关键词： Categorical response MCMC Outlier saem algorithm Scale mixing

来源：评论

学校读者我要写书评

暂无评论

REML estimation of variance parameters in nonlinear mixed effects models using the saem algorithm

引用

BIOMETRICAL JOURNAL 2007年第6期49卷 876-888页

作者： Meza, Cristian Jaffrezic, Florence Foulley, Jean-Louis Univ Paris 11 Math Lab F-91405 Orsay France Univ Valparaiso Dept Estadist Valparaiso 1091 Chile INRA Quantitat & Appl Genet F-78352 Jouy En Josas France

Nonlinear mixed effects models are now widely used in biometrical studies, especially in pharmacokinetic research or for the analysis of growth traits for agricultural and laboratory species. Most of these studies, however, are often based on ML estimation procedures, which are known to be biased downwards. A few REML extensions have been proposed, but only for approximated methods. The aim of this paper is to present a REML implementation for nonlinear mixed effects models within an exact estimation scheme, based on an integration of the fixed effects and a stochastic estimation procedure. This method was implemented via a stochastic EM, namely the saem algorithm. The simulation study showed that the proposed REML estimation procedure considerably reduced the bias observed with the ML estimation, as well as the residual mean squared error of the variance parameter estimations, especially in the unbalanced cases. ML and REML based estimators of fixed effects were also compared via simulation. Although the two kinds of estimates were very close in terms of bias and mean square error, predictions of individual profiles were clearly improved when using REML vs. ML. An application of this estimation procedure is presented for the modelling of growth in lines of chicken.

关键词： nonlinear mixed models REML estimation saem algorithm variance components

来源：评论

学校读者我要写书评

暂无评论

PARAMETER ESTIMATION IN NON-LINEAR MIXED EFFECTS MODELS WITH saem algorithm: EXTENSION FROM ODE TO PDE

引用

ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS-MODELISATION MATHEMATIQUE ET ANALYSE NUMERIQUE 2014年第5期48卷 1303-1329页

作者： Grenier, E. Louvet, V. Vigneaux, P. Ecole Normale Super Lyon CNRS UMR 5669 UMPA F-69364 Lyon 07 France INRIA Project Team NUMED F-69364 Lyon 07 France Inst Camille Jordan CNRS UMR 5208 F-69622 Villeurbanne France Univ Lyon 1 F-69622 Villeurbanne France INRIA Project Team NUMED F-69622 Villeurbanne France

Parameter estimation in non linear mixed effects models requires a large number of evaluations of the model to study. For ordinary differential equations, the overall computation time remains reasonable. However when the model itself is complex (for instance when it is a set of partial differential equations) it may be time consuming to evaluate it for a single set of parameters. The procedures of population parametrization (for instance using saem algorithms) are then very long and in some cases impossible to do within a reasonable time. We propose here a very simple methodology which may accelerate population parametrization of complex models, including partial differential equations models. We illustrate our method on the classical KPP equation.

关键词： Parameter estimation saem algorithm partial differential equations KPP equation

来源：评论

学校读者我要写书评

暂无评论

Extension of the saem algorithm to left-censored data in nonlinear mixed-effects model:: Application to HIV dynamics model

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第3期51卷 1562-1574页

作者： Samson, Adeline Lavielle, Marc Mentre, France INSERM U738 Paris France Univ Paris 07 UFR Med Paris France Univ Paris 05 Paris France Univ Paris 11 Orsay France Hop Bichat Claude Bernard AP HP Dept Epidemiol Biostat & Rech Clin F-75877 Paris France

The reduction of viral load is frequently used as a primary endpoint in HIV clinical trials. Nonlinear mixed-effects models are thus proposed to model this decrease of the viral load after initiation of treatment and to evaluate the intra- and inter-patient variability. However, left censoring due to quantification limits in the viral load measurement is an additional challenge in the analysis of longitudinal HIV data. An extension of the stochastic approximation expectation-maximization (saem) algorithm is proposed to estimate parameters of these models. This algorithm includes the simulation of the left-censored data in a right-truncated Gaussian distribution. Simulation results show that the proposed estimates are less biased than the usual naive methods of handling such data: omission of all censored data points, or imputation of half the quantification limit to the first point below the limit and omission of the following points. The viral load measurements obtained in the TRIANON-ANRS81 clinical trial are analyzed with this method and a significant difference is found between the two treatment groups of this trial. (c) 2006 Elsevier B.V. All rights reserved.

关键词： accept-reject algorithm HIV dynamics left-censored data MCMC algorithm nonlinear mixed-effects models saem algorithm

来源：评论

学校读者我要写书评

暂无评论

Performance in population models for count data, part II: A new saem algorithm

引用

JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS 2009年第4期36卷 367-379页

作者： Savic, Radojka Lavielle, Marc Univ Paris 07 INSERM UMR 738 F-75018 Paris France INRIA Saclay Math Lab F-91400 Orsay France Univ Paris 11 F-91400 Orsay France

Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (saem) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new saem algorithm for application to count data. A new saem algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (Plan et al., 2008, Abstr 1372 [http://wwwpage-meetingorg/?abstract=1372]). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13% for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being < 1.7% for all explored scenarios. The longest CPU time was 95 s for parameter estimation and 56 s for SE estimation. The saem algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009).

关键词： Nonlinear mixed effects model Count data Maximum likelihood estimation Fischer information matrix saem algorithm MONOLIX

来源：评论

学校读者我要写书评

暂无评论

A saem algorithm for the estimation of template and deformation parameters in medical image sequences

引用

STATISTICS AND COMPUTING 2009年第4期19卷 465-478页

作者： Richard, Frederic J. P. Samson, Adeline M. M. Cuenod, Charles A. Univ Paris 05 Dept Math MAP5 CNRSUMR 8145 Paris France Univ Paris 05 Serv Radiol Hosp Georges Pompidou LRIEA4062 Paris France

This paper is about object deformations observed throughout a sequence of images. We present a statistical framework in which the observed images are defined as noisy realizations of a randomly deformed template image. In this framework, we focus on the problem of the estimation of parameters related to the template and deformations. Our main motivation is the construction of estimation framework and algorithm which can be applied to short sequences of complex and highly-dimensional images. The originality of our approach lies in the representations of the template and deformations, which are defined on a common triangulated domain, adapted to the geometry of the observed images. In this way, we have joint representations of the template and deformations which are compact and parsimonious. Using such representations, we are able to drastically reduce the number of parameters in the model. Besides, we adapt to our framework the Stochastic Approximation EM algorithm combined with a Markov Chain Monte Carlo procedure which was proposed in 2004 by Kuhn and Lavielle. Our implementation of this algorithm takes advantage of some properties which are specific to our framework. More precisely, we use the Markovian properties of deformations to build an efficient simulation strategy based on a Metropolis-Hasting-Within-Gibbs sampler. Finally, we present some experiments on sequences of medical images and synthetic data.

关键词： saem algorithm Medical image sequence Template estimation Image registration Image denoising

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：