版上以前有些NONMEM相关的讨论。不知多少人对Pharmacometrics及NONMEM感兴趣？有兴趣的话，请签个到，看看我们能不能建个讨论群之类的。或许我们将来也能弄个PAGE或ACoP之类的组织。
顶一下
这么好的事儿，顶，我正想学习！
顶一下
NONMEM VII: Improvements and New Estimation Methods for Population Analysis of PK/PD Problems
NONMEM is presently being greatly enhanced, incorporating the latest proven statistical methods in population analysis for pharmacokinetic/pharmacodynamic (PK/PD) problems. Exact likelihood expectation-maximization (EM) methods such as Monte Carlo importance sampling EM and Markov chain Monte Carlo (MCMC) stochastic approximation EM, as well as MCMC Bayesian methods, are being implemented in NONMEM VII to obtain parameter estimates that are less biased than nonlinear mixed effects maximization methods that utilize linearization approximation techniques. Also, the MCMC Bayesian method allows the user to obtain a distribution of probable population parameters, from which various summary statistics may be obtained, such as means, standard errors, and percentile ranges. Bayesian standard errors of parameters are obtained with guaranteed positive definiteness and numerical stability. The stability of these new methods and the ease with which to use them in NONMEM VII will be demonstrated. Presently existing methods and algorithms are also being enhanced, such as improvement in the first order conditional estimation method (FOCE), numerical integration, continuation of superproblems even when certain error conditions are encountered for a given problem. Finally, all native source code and IMSL routines are being updated to Fortran 95.
S-ADAPT as an innovative tool for Model-based Drug Development
Development of population PKPD models using NONMEM for model-based drug development is both resource and time intensive. In the last ten years, a series of new tools for population PK/PD modeling and simulation have become available. These include methods based on exact likelihood functions such as Monte-Carlo Parametric Expectation Maximization (MCPEM) and Stochastic Approximation Expectation Maximization (SAEM) algorithms, and three-stage Bayesian methods. S-ADAPT is a software package that incorporates many of these state-of-the-art estimation methods for model development in a user-interactive as well as scriptable environment, and has been used successfully in analyzing complex population PK/PD data. In this presentation, the general features and different estimation methods for population model development will be highlighted and demonstrated. Then the concept of distributive computing capability of S-ADAPT will be introduced. Finally, cases will be present to demonstrate the feasibility of using S-ADAPT in analyzing complex population PKPD data.
是不是真的.nonmem出了vii了,我刚刚从nmv升级到nmvi,是不是又落伍了..
搞错了吧..不过,我目前学习这个软件呢.单位是南京军区总医院..这里有很多我认识的人,就不一一打招呼了.
我们组正在协助测试NM7 BETA版。难度较以前大了很多。
Introduction to NONMEM 7
Date: Thursday, October 8, 2009
Registration fee: Non-student ($500), Student/fellow ($50)
Maximum class size: 75
Sponsor: ICON Development Solutions
Instructors/Teaching Assistants: Bob Bauer and Bill Bachman
Workshop summary:
This one day workshop will cover the description and use of new features in NONMEM 7. The NONMEM 7 software has been significantly upgraded from NONMEM VI to meet the demands of population PK/PD modeling. The classical NONMEM algorithm first order conditional estimation method (FOCE) has been improved by reducing the occurrence of computational problems that result in abnormal termination.
Workshop attendees will be instructed how to specify gradient precision, which improves the efficiency of optimization and increases the incidence of successful completion of the problem. Workshop attendees will also be instructed on how to use the new estimation methods, such as iterative two stage (ITS), importance sampling expectation maximization (EM), Markov chain Monte Carlo (MCMC) stochastic approximation EM (SAEM), and three hierarchical stage MCMC Bayesian method using Gibbs and Metropolis-Hastings algorithms. All set-up parameters for these new methods may be specified in the standard NMTRAN control stream file format. Demonstrations will show that NONMEM 7 has the ability to handle more data file items, longer labels, and initial parameters may be expressed in any numerical format. Output files that are readily transferred to post- processing software are also produced, and the number of significant digits reported may be specified by the user. Attendees will also learn how to obtain diagnostic results such as inter-subject and residual variance shrinkage, conditional weighted residuals, Monte Carlo assessed exact weighted residuals, and normalized probability distribution errors. 3 1 2 3 下一页 尾页
顶一下
这么好的事儿，顶，我正想学习！
顶一下
NONMEM VII: Improvements and New Estimation Methods for Population Analysis of PK/PD Problems
NONMEM is presently being greatly enhanced, incorporating the latest proven statistical methods in population analysis for pharmacokinetic/pharmacodynamic (PK/PD) problems. Exact likelihood expectation-maximization (EM) methods such as Monte Carlo importance sampling EM and Markov chain Monte Carlo (MCMC) stochastic approximation EM, as well as MCMC Bayesian methods, are being implemented in NONMEM VII to obtain parameter estimates that are less biased than nonlinear mixed effects maximization methods that utilize linearization approximation techniques. Also, the MCMC Bayesian method allows the user to obtain a distribution of probable population parameters, from which various summary statistics may be obtained, such as means, standard errors, and percentile ranges. Bayesian standard errors of parameters are obtained with guaranteed positive definiteness and numerical stability. The stability of these new methods and the ease with which to use them in NONMEM VII will be demonstrated. Presently existing methods and algorithms are also being enhanced, such as improvement in the first order conditional estimation method (FOCE), numerical integration, continuation of superproblems even when certain error conditions are encountered for a given problem. Finally, all native source code and IMSL routines are being updated to Fortran 95.
S-ADAPT as an innovative tool for Model-based Drug Development
Development of population PKPD models using NONMEM for model-based drug development is both resource and time intensive. In the last ten years, a series of new tools for population PK/PD modeling and simulation have become available. These include methods based on exact likelihood functions such as Monte-Carlo Parametric Expectation Maximization (MCPEM) and Stochastic Approximation Expectation Maximization (SAEM) algorithms, and three-stage Bayesian methods. S-ADAPT is a software package that incorporates many of these state-of-the-art estimation methods for model development in a user-interactive as well as scriptable environment, and has been used successfully in analyzing complex population PK/PD data. In this presentation, the general features and different estimation methods for population model development will be highlighted and demonstrated. Then the concept of distributive computing capability of S-ADAPT will be introduced. Finally, cases will be present to demonstrate the feasibility of using S-ADAPT in analyzing complex population PKPD data.
是不是真的.nonmem出了vii了,我刚刚从nmv升级到nmvi,是不是又落伍了..
搞错了吧..不过,我目前学习这个软件呢.单位是南京军区总医院..这里有很多我认识的人,就不一一打招呼了.
我们组正在协助测试NM7 BETA版。难度较以前大了很多。
Introduction to NONMEM 7
Date: Thursday, October 8, 2009
Registration fee: Non-student ($500), Student/fellow ($50)
Maximum class size: 75
Sponsor: ICON Development Solutions
Instructors/Teaching Assistants: Bob Bauer and Bill Bachman
Workshop summary:
This one day workshop will cover the description and use of new features in NONMEM 7. The NONMEM 7 software has been significantly upgraded from NONMEM VI to meet the demands of population PK/PD modeling. The classical NONMEM algorithm first order conditional estimation method (FOCE) has been improved by reducing the occurrence of computational problems that result in abnormal termination.
Workshop attendees will be instructed how to specify gradient precision, which improves the efficiency of optimization and increases the incidence of successful completion of the problem. Workshop attendees will also be instructed on how to use the new estimation methods, such as iterative two stage (ITS), importance sampling expectation maximization (EM), Markov chain Monte Carlo (MCMC) stochastic approximation EM (SAEM), and three hierarchical stage MCMC Bayesian method using Gibbs and Metropolis-Hastings algorithms. All set-up parameters for these new methods may be specified in the standard NMTRAN control stream file format. Demonstrations will show that NONMEM 7 has the ability to handle more data file items, longer labels, and initial parameters may be expressed in any numerical format. Output files that are readily transferred to post- processing software are also produced, and the number of significant digits reported may be specified by the user. Attendees will also learn how to obtain diagnostic results such as inter-subject and residual variance shrinkage, conditional weighted residuals, Monte Carlo assessed exact weighted residuals, and normalized probability distribution errors. 3 1 2 3 下一页 尾页