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Bayesian Modeling and Simulation
Bayesian modeling and simulation methods are particularly advantageous for making inferences based on a combination of new data and prior information. Bayesian statistics and decision analysis also provide a framework for flexible clinical trial strategies such as adaptive trial designs.
Bayesian methods provide the basis for more rigorous and flexible approaches to incremental model building, parameter estimation and other statistical inference as knowledge and data are accumulated during a drug development program. They also provide a rational approach to dealing with uncertainty. The recent development of Monte Carlo Markov Chain simulation methods has made it possible to apply complex hierarchical Bayesian models to population PK and PK/PD modeling and simulation.