Dear Marc, I am sorry, but I am missing your boat. You wrote: For example:
Instead of: CL = THETA(1)*(WT/70)**THETA(2)*EXP(ETA(1)) Parameterize as: LNCL = THETA(1)+THETA(2)*(WT/70)+ETA(1) CL = EXP(LNCL) This sort of transformation is a useful thing to do for NONMEM simulation and estimation in general, because it creates a parameter uncertainty distribution that is consistent (for THETA) with the MVN assumption implicit in Maximum Likelihood methods for continuous data. This means that confidence intervals (for THETA) from NONMEM's asymptotic standard errors ($COV) should be more realistic. You may also find improved stability in estimation runs. Best regards, Marc How can your first line of your code ever result in negative CL. I have adopted the log-transformation of data before estimation (thanks to Matts for promoting this!), but I cannot see the reason why to log-transform parameters before simulation when I use proportional error terms. Thanks, Joachim _________________________________ AstraZeneca R&D Charnwood Clin. Pharmacology and DMPK Bakewell Road Loughborough, LE11 5RH Tel: +44 1509 644035 [email protected] -------------------------------------------------------------------------- AstraZeneca UK Limited is a company incorporated in England and Wales with registered number: 03674842 and a registered office at 15 Stanhope Gate, London W1K 1LN. Confidentiality Notice: This message is private and may contain confidential, proprietary and legally privileged information. If you have received this message in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorised use or disclosure of the contents of this message is not permitted and may be unlawful. Disclaimer: Email messages may be subject to delays, interception, non-delivery and unauthorised alterations. Therefore, information expressed in this message is not given or endorsed by AstraZeneca UK Limited unless otherwise notified by an authorised representative independent of this message. No contractual relationship is created by this message by any person unless specifically indicated by agreement in writing other than email. Monitoring: AstraZeneca UK Limited may monitor email traffic data and content for the purposes of the prevention and detection of crime, ensuring the security of our computer systems and checking Compliance with our Code of Conduct and Policies. -----Original Message----- From: [email protected] [mailto:[email protected]]on Behalf Of Gastonguay, Marc Sent: 16 July 2009 18:59 To: nmusers Subject: Re: AW: [NMusers] Simulations with/without residual error (Apologies for the delayed posting.. this apparently didn't make it to nmusers on the initial attempt). Dear Nick, Andreas, Andreas and nmusers, Here are a couple of additional methods for including uncertainty in parameters at the inter-trial (or inter-replicate) level, when simulating with NONMEM: 1. You can take advantage the PRIOR subroutine in NONMEM VI (and VII - although I haven't tried it yet) simulations, to generate random variates from a Multi-Variate Normal distribution for THETA and an Inverse Wishart distribution for OMEGA. This works fine if your prior uncertainty distributions are adequately described by these distributions. Of course the MVN assumption is consistent with the var-covar matrix of the estimates in NONMEM, but you'll have to translate the uncertainty in OMEGA into the required parameters of an Inv. Wishart (e.g. mode and degrees of freedom). This method does not directly allow for prior uncertainty on SIGMA. 2. If you'd like to simulate from other distributions, or pull-in uncertainty in parameter estimates from other sources, such as the resulting parameter estimates from bootstrap replicates or MCMC Bayesian posterior distributions, you'll need to use an external tool with NONMEM. As Andreas points out, R is a useful choice. Leonid Gibiasnky and I had developed a toolkit of R functions called NMSUDS to facilitate these types of simulations in NONMEM. These functions have been extended and are now part of the broader MIfuns package ( http://cran.r-project.org/). There's another important issue to consider... Be careful that the specification of the prior uncertainty distribution is consistent with reality for the parameters in your model. This point has been discussed by Pascal Girard and others in past nmusers threads. For example, a MVN uncertainty distribution for THETA is not realistic for PK parameters and is never realistic for OMEGA and SIGMA, in that MVN allows for simulation of negative values. To work-around this problem for THETA, you could choose to log-transform typical values of PK parameters to constrain resulting replicates within a physiologically realistic range. For example: Instead of: CL = THETA(1)*(WT/70)**THETA(2)*EXP(ETA(1)) Parameterize as: LNCL = THETA(1)+THETA(2)*(WT/70)+ETA(1) CL = EXP(LNCL) This sort of transformation is a useful thing to do for NONMEM simulation and estimation in general, because it creates a parameter uncertainty distribution that is consistent (for THETA) with the MVN assumption implicit in Maximum Likelihood methods for continuous data. This means that confidence intervals (for THETA) from NONMEM's asymptotic standard errors ($COV) should be more realistic. You may also find improved stability in estimation runs. Best regards, Marc Marc R. Gastonguay, Ph.D. < [email protected] > President & CEO, Metrum Research Group LLC < metrumrg.com > Scientific Director, Metrum Institute < metruminstitute.org > 2 Tunxis Rd, Suite 112, Tariffville, CT 06081 Direct: +1.860.670.0744 Main: +1.860.735.7043 Fax: +1.860.760.6014
