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Drug development, aimed at improving people’s health, becomes more costly every year. The pharmaceutical industry must join its efforts with government and the health professions to seek new, innovative, and costeffective approaches in the development process. During this evolutionary process over the next decades, computer simulations will no doubt play a critical role. Computer simulation or Monte Carlo simulation is the technique of simulating a dynamic system or process using a computer program. Computer simulations, as an efficient and effective research tool, have been used in virtually every area of engineering, science, mathematics, etc.
In this book, I present concepts, theories, algorithms, and cases studies of Monte Carlo simulation in the pharmaceutical and health industries. The concepts refer not only to simulation in general, but also to various types of simulations in drug development. The theory will include virtual data sampling, game theory, deterministic and stochastic decision theories, adaptive design methods, Petri net, genetic programming, resampling methods, and other strategies. These theories and methods are necessary either to carry out the simulations or to make the simulations more efficient, even though there are many practical problems that can be simulated directly in an ad hoc fashion without any theory about their efficiency or convergence considerations. The algorithms, which can be descriptive, computer pseudocode, or a combination of both, provide the basis for implementation of simulation methods. The case studies or applications are simplified versions of real-world problems. These simplifications are necessary because a single case could otherwise occupy the whole book, preventing readers from exploring broad issues. There are also examples of how simulation can be formulated to address interesting questions that have not been studied before. In my view, building simulation models is, to a large extent, a creative process; it is an innovation in application, which can impact our lives more positively than other innovations or creations. Simulation often requires knowledge in several disciplines, including mathematics/statistics, programming, and the subject field.
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