Software Bayesian

The commercially available software (Maximum Entropy Data Consultant Ltd, Cambridge, UK) allows reconstruction of the distribution a.(z) (or f(z)) which has the maximal entropy S subject to the constraint of the chi-squared value. The quantified version of this software has a full Bayesian approach and includes a precise statement of the accuracy of quantities of interest, i.e. position, surface and broadness of peaks in the distribution. The distributions are recovered by using an automatic stopping criterion for successive iterates, which is based on a Gaussian approximation of the likelihood. 

Devlin, B., and Roeder, K. (1999) Genomic control for association studies. Biometrics. 55, 997-1004. Available at http //wpicr.wpic.pitt.eduAVPICCompGen/bayesian genomic control softwar.htm. 

Efforts should be made to provide and improve user-friendly software, especially for those approaches where it currently appears to be lacking (e.g., Bayesian methods and Monte Carlo with more than 2 dimensions). 

Other multivariate methods have been applied to ICP spectra for quantitative measurements. As examples, they include multicomponent spectral fitting (which is incorporated in several commercial instrument software) [81] matrix projection, which avoids measurement of background species [94,95] generalised standard additions [96] and Bayesian analysis [97]. 

Jonsson F, Johanson G. 2003. The Bayesian population approach to physiological toxicoki-netic-toxicodynamic models—an example using the MCSim software. Toxicol Lett 138 143-150. 

Bayesian analysis provides another alternative (also computationally intensive) to deal with very weak signals and avoid FT artifacts. This method, which uses probability theory to estimate the value of spectral parameters, permits prior information to be included in the analysis, such as the number of spectral lines when known or existence of regular spacings from line splittings due to spin coupling. Commercial software is available for Bayesian analysis, and the technique is useful in certain circumstances. 

Lunn, D.J. Best, N. Thomas, A. Wakefield, L Spiegelhalter, D. Bayesian analysis of population PK/ PD models general concepts and software. J. Pharmaco-kinet. Pharmacodyn. 2002, 29 (3), 271-307. 

Mixed effects models under a Bayesian framework have been widely studied and used with the use of Markov chain Monte Carlo methods (10). These methods have gained particular popularity as complex problems became easily formulated using the WinBUGS software (11). See Congdon (12) for an extensive coverage of topics and examples and implementation in WinBUGS. 

D. J. Lunn, N. Best, A. Thomas, J. Wakefield, and D. Spiegelhalter, Bayesian analysis of population PK/PD models general concepts and software. I Pharmacokinet Pharmacodyn 29 271-307 (2002). 

Maitre et al. (15) proposed an improvement on the traditional approach. The approach consists of using individual Bayesian posthoc PK or PK/PD parameters from a population modeling software such as NONMEM and plotting these parameter estimates against covariates to look for any possible model parameter covariate relationship. The individual model parameter estimates are obtained using a base model—a model without covariates. The covariates are in turn tested to determine individual significant covariate predictors, which are in turn used to form a full model. The final irreducible model is obtained by backward elimination. The drawback for this approach is the same as that for the traditional approach. 

The three disadvantages of MI when compared with other imputation methods are (a) more effort to create the multiple imputations, (b) more time to run the analyses, and (c) more computer storage space for Ml-created data sets (6). These are hardly issues with current development in computer technology. The MI approach is computationally simpler than the ML and Bayesian approaches for most practical situations. Once the imputed data is generated, the data can be analyzed with any data analysis software of choice. 

This ability is available in many software programs. NONMEM (Iconus, EUicott City, MD) has been widely used to estimate population models arising from both sparse and intensely sampled data. Other programs include WinNonMix (Pharsight Corp., Palo Alto, CA), Kinetica 2000 (Innaphase Corp, Philadelphia, PA), and Pop-Kinetics (SAAM Institute, Seattle, WA). ADAPT II and WinNonlin have focused on PK/PD models and have been combined with Bayesian approaches to estimate population models. 

S. Zohar, A. Latouche, M. Taconnet, and S. Chevret, Software to compute and conduct sequential Bayesian phase I or II dose-ranging clinical trials with stopping rules. Comput Methods Programs Biomed 72 117-125 (2003). 

Once a PBPK model is developed and implemented, it should be tested for mass balance consistency, as weU as through simulated test cases that can highlight potential errors. These test cases often include software boundary conditions, such as zero dose and high initial tissue concentrations. Some parameters in the PBPK model may have to be estimated through available in vivo data via standard techniques such as nonlinear regression or maximum likelihood estimation (30). Furthermore, in vivo data can be used to update existing (or prior) PBPK model parameter estimates in a Bayesian framework, and thus help in the rehnement of the PBPK model. The Markov chain Monte Carlo (MCMC) (31-34) is one of the... 

---