Abstract
Stochastic differential equation mixed effects models (SDEMEMs) are increasingly used in biomedical and pharmacokinetic/pharmacodynamic research. However, the complexity of these models means that previous research has focussed on approximate parameter estimation methods. This thesis develops three novel Bayesian parameter estimation methods for SDEMEMs. The new methods can produce parameter estimates that are more accurate and provide more reliable uncertainty quantification. The new methods are applied to both real and simulated data from a tumour xenography study on mice.
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