Model independent method

These data were calculated by the model-independent methods described in the text by Gibaldi and Perrier (1982). Since we were interested in calculating the effect of anti-PCP Fab fragments on... 

The second approach to optimisation is a model independent one. One of these model independent methods is the sequential simplex [24,25] used by Shek et al. [26], The method is claimed to be ideally suited for the optimisation of formulations [27] because of the relatively low number of experiments to be performed. 

An important limitation of compartment analysis is that it cannot be applied universally to any drug. A simpler approach that is useful in the case of bioequivalency testing is the model independent method. It is based on statistical-moment theory. This approach uses the mean residence time (MRT) as a measure of a statistical half-life of the drug in the body. The MRT can be calculated by dividing the area under the first-moment curve (AUMC) by the area under the plasma curve (AUC). ... 

A second approach uses the unimodal model-independent method, which begins with the assumption that the size distribution consists of a finite number of fixed size classes. The detector response expected for this distribution is simulated, and then the weight fractions in each size class are optimized through a minimization of the sum of squared deviations from the measured and simulated detector responses. The third system uses the multimodal model-independent method. For this, diffraction patterns for known size distributions are simulated, random noise is superimposed on the patterns, and then the expected element responses for the detector configuration are calculated. The patterns are inverted by the same minimization algorithm, and these inverted patterns are compared with known distributions to check for qualitative correctness. 

P. Veng-Pederson, Model-independent method of predicting peak, trough, and mean steady-state levels in multiple intravenous bolus dosing in nonlinear pharmacokinetics. / Pharm Sci 72 1098-1100 (1983). 

In instance based learning the training data is used directly and predictions are done by taking some consensus value of the nearest training set points. Sometimes called model independent methods, or naive models, they are conceptually simple and explicit examples of the well known similarity principle, which is the hypothesis that chemically similar compounds have similar properties. The diversity of methods available derives from the different choices to be made regarding how chemical space is defined and which distance metric is used. 

Piotrovskii, V. K., Model and model-independent methods of describing pharmacokinetics the advantages, drawbacks and interrelationship, Antibiot. Med. BiotekhnoL, 32 492 97, 1987. 

Saltelli, A., S. Tarantola, and K. Chan (1999). A quantitative, model independent method for global sensitivity analysis of model output. Technometrics 41, 39-56. 

Saltelli, A., Tarantola, S. Chan, K. 1999. A Quantitative, Model Independent Method for Global Sensitivity Analysis of Model Output. Technometrics 41(1) 39-56. 

The success of such data analysis approach is necessarily linked to the reliability of the model chosen to describe the system. This limited the use of this method of interpretation in the study of blends, in favour of more model-independent methods, like the Porod and Debye-Bueche described above. However, some examples of the use of Equation (21.13) may be found in the literature. Micellar systems of block copolymers dispersed in a polyisoprene matrix were modelled by Pavlopoulos et al7 with the form factor of a homogeneous sphere, multiplied by a function accounting for the poly-dispersity in the micelles. In this case, the structure factor was neglected, due to the extreme dilution of the system. 

Chain-Length Dependent Bimolecular Termination in Free-Radical Polymerization Theory, Validation and Experimental Application of Novel Model-Independent Methods... 

Model-independent method for non-isothermal experiments Model-dependent methods need prior knowledge of mathematical function of fractional decomposition, f(a) as reported in the literature [45-47]. Iso-conversion method eliminates the need of assumption of a mathematical functional form, f(a) [52-55]. 

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