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• W970922265 abstract "In this thesis PFIM, a software used for evaluation and optimization of clinical studies based on the Fisher information matrix, is investigated. The program is evaluated using three different models, of which two are PK/PD models and the third is a model describing glucose-insulin regulation in the absence of drug. For an analytical PK/PD model describing therapeutic response the expected standard errors of the parameter estimates given by PFIM correspond to the empirical standard errors obtained from a large simulation in NONMEM. PFIM offers a fast and effective way of calculating the expected standard errors for the parameter estimates using an approximation of the Fisher information matrix. In the glucose-insulin model it is showed that the number of measurements per patient can be decreased while maintaining the same information criterion. The other PK/PD model describes a long-term safety marker with turnover response where the input rate varies over time. The results indicate that PFIM has problem with long running time if a model is specified by an ordinary differential equation system. In conclusion PFIM can be used to evaluate, reduce and improve designs for clinical studies. For analytical models, the expected standard errors are appropriate. If the model is described by an ordinary differential equation system the expected standard errors obtained from PFIM are slightly lower compared to empirical standard errors. The recommendation is to use PFIM for evaluating and comparing suggested design alternatives. However, the selected design should be evaluated using simulation techniques. Acknowledgements This thesis has been done in collaboration with Clinical Pharmacology Sciences and Statistics & Programming at AstraZeneca R&D, Molndal. I would like to thank my examiner Ziad Taib and my supervisor Daniel Roshammar. I have a learned a lot from all the fruitful discussions we had during my work at AstraZeneca. Jacob Leander, Gothenburg June 7, 2012 ”All models are wrong, but some are useful” George E.P Box Abbreviations and terms ka Absorption rate Cl Clearance C50 Concentration needed to achieve half of the maximal effect ω2 Diagonal element of Ω D Dose C Drug concentration ke Elimination rate FO First order FOCE First order conditional estimation β Fixed parameter vector ξ Individual design Emax Maximal effect MBDD Model based drug development V Volume of distribution Ω Parameter covariance structure PD Pharmacodynamics PK Pharmacokinetics Ξ Population design λ Random parameter vector Σ Residual covariance structure SAEM Stochastic approximation expectation maximization" @default.
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• W970922265 date "2012-01-01" @default.
• W970922265 modified "2023-09-24" @default.
• W970922265 title "Optimal design of clinical studies using the PFIM software" @default.
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