Generalized nonlinear models for pharmacokinetic data
| Autor: | James K. Lindsey, P. Jarvis, Bradley Jones, Jihui Wang, W. D. Byrom |
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| Rok vydání: | 2000 |
| Předmět: |
Statistics and Probability
Adult Mathematical optimization Biometry Metabolite Vasodilator Agents Models Biological General Biochemistry Genetics and Molecular Biology chemistry.chemical_compound medicine Gamma distribution Humans Pharmacokinetics Flosequinan Mathematics General Immunology and Microbiology Applied Mathematics Log-Cauchy distribution General Medicine Random effects model Log-Laplace distribution Nonlinear system chemistry Nonlinear Dynamics Censoring (clinical trials) Quinolines Regression Analysis General Agricultural and Biological Sciences medicine.drug |
| Zdroj: | Biometrics. 56(1) |
| ISSN: | 0006-341X |
| Popis: | SUMMARY. Phase I trials to study the pharmacokinetic properties of a new drug generally involve a restricted number of healthy volunteers. Because of the nature of the group involved in such studies, the appropriate distributional assumptions are not always obvious. These model assumptions include the actual distribution but also the ways in which the dispersion of responses is allowed to vary over time and the fact that small concentrations of a substance are not easily detectable and hence are left censored. We propose that a reasonably wide class of generalized nonlinear models allowing for left censoring be considered now that this is feasible with current computer power and sophisticated statistical packages. These modelling strategies are applied to a Phase I study of the drug flosequinan and its metabolite. This drug was developed for the treatment of heart failure. Because the metabolite also exhibits an active pharmacologic effect, study of both the parent drug and the metabolite is of interest. |
| Databáze: | OpenAIRE |
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