Bug in nonmem
In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. Both datasets were originally analyzed by using NONMEM.
The second dataset incorporated data from 96 patients receiving enoxaparin. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. Wiley, Chichester.The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. Graphical Models in Applied Multivariate Analysis. In Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. Spatial dependence and errorsin-variables in environmental epidemiology. Component-oriented programming: A refined variation of object-oriented programming. Medical Research Council Biostatistics Unit, Cambridge. BUGS 0.5: Bayesian inference Using Gibbs Sampling-Manual (version ii). Spiegelhalter D., Thomas A., Best N., and Gilks W. Computation on Bayesian graphical models. Spiegelhalter D.J., Thomas A., and Best N.G. Bayesian analysis in expert systems (with discussion). Spiegelhalter D.J., Dawid A.P., Lauritzen S.L., and Cowell R.G. (Eds.), Markov Chain Monte Carlo in Practice. In Gilks W.R., Richardson S., and Spiegelhalter D.J. Hepatitis B: A case study in MCMC methods. Spiegelhalter D.J., BestN.G., GilksW.R., and Inskip H. Bayesian graphical modelling: A case-study in monitoring health outcomes. Programming in Oberon: Steps Beyond Pascal and Modula. Time-varying covariances: A factor stochastic volatility approach. Component Software: A Case Study Using Black Box Components. Markov chain Monte Carlo methods based on 'slicing' the density function.
#Bug in nonmem software
Object Oriented Software Construction, 2nd Edition. Journal of Chemical Physics 21: 1087–1091. Equations of state calculations by fast computing machines. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. Epidemiology and Public Health, Imperial College School of Medicine, London. Lunn D.J., Wakefield J., Thomas A., Best N., and Spiegelhalter D. Journal of Pharmacokinetics and Biopharmaceutics 26: 47–74. The pharmacokinetics of saquinavir: A Markov chain Monte Carlo population analysis. Independence properties of directed Markov fields. Lauritzen S.L., Dawid A.P., Larsen B.N., and Leimer H.G. Monte Carlo sampling-based methods using Markov chains and their applications. Adaptive rejection sampling for Gibbs sampling. GilksW.R., Richardson S., and Spiegelhalter D.J. In: Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. Derivative-free adaptive rejection sampling for Gibbs sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721–741. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Journal of the American Statistical Association 85: 398–409. Sampling-based approaches to calculating marginal densities. Bayesian computation and stochastic systems. NONMEM Project Group, San Francisco.īesag J., Green P., Higdon D., and Mengersen K. New England Journal of Medicine 338: 1896–1904.īeal S.L. Rating the appropriateness of coronary angiography-Do practicing physicians agree with an expert panel and with each other?. Neither of these types of extension require access to, or even recompilation of, the WinBUGS source-code.Īyanian J.Z., Landrum M.B., Normand S.-L.T., Guadagnoli E., and McNeil B.J. It is also possible to interface with WinBUGS at a lower level by incorporating new object types that may be used by WinBUGS without knowledge of the modules in which they are implemented. It is possible to write specific applications that form an apparently seamless interface with WinBUGS for users with specialized requirements. We also discuss how the framework may be extended. In this paper we discuss how and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design.
The software offers a user-interface, based on dialogue boxes and menu commands, through which the model may then be analysed using Markov chain Monte Carlo techniques. WinBUGS processes the model specification and constructs an object-oriented representation of the model. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS.
#Bug in nonmem full
WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models.