Simultaneous versus sequential optimal design for pharmacokinetic-pharmacodynamic models with FO and FOCE considerations |
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Authors: | J M McGree J A Eccleston S B Duffull |
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Institution: | (1) School of Physical Science, University of Queensland, St. Lucia, Brisbane, 4072, Australia;(2) School of Pharmacy, University of Otago, Dunedin, 9001, New Zealand |
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Abstract: | We consider nested multiple response models which are used extensively in the area of pharmacometrics. Given the conditional
nature of such models, differences in predicted responses are a consequence of different assumptions about how the models
interact. As such, sequential versus simultaneous and First Order (FO) versus First Order Conditional Estimation (FOCE) techniques
have been explored in the literature where it was found that the sequential and FO approaches can produce biased results.
It is therefore of interest to determine any design consequences between the various methods and approximations. As optimal
design for nonlinear mixed effects models is dependent upon initial parameter estimates and an approximation to the expected
Fisher information matrix, it is necessary to incorporate any influence of nonlinearity (or parameter-effects curvature) into
our exploration. Hence, sequential versus simultaneous design with FO and FOCE considerations are compared under low, typical
and high degrees of nonlinearity. Additionally, predicted standard errors of parameters are also compared to empirical estimates
formed via a simulation/estimation study in NONMEM. Initially, design theory for nested multiple response models is developed
and approaches mentioned above are investigated by considering a pharmacokinetic–pharmacodynamic model found in the literature.
We consider design for situations where all responses are continuous and extend this methodology to the case where a response
may be a discrete random variable. In particular, for a binary response pharmacodynamic model, it is conjectured that such
responses will offer little information about all parameters and hence a sequential optimization, in the form of product design
optimality, may yield near optimal designs. |
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Keywords: | D-optimality Discrete response FO and FOCE approximations Nested multiple response models Product design optimality Simultaneous versus sequential design |
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