Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies / Edition 1

Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies / Edition 1

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by Mark Chang
     
 

Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods

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Overview

Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods needed to carry out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementation of the simulation methods, and illustrates real-world problems through case studies.

The text first emphasizes the importance of analogy and simulation using examples from a variety of areas, before introducing general sampling methods and the different stages of drug development. It then focuses on simulation approaches based on game theory and the Markov decision process, simulations in classical and adaptive trials, and various challenges in clinical trial management and execution. The author goes on to cover prescription drug marketing strategies and brand planning, molecular design and simulation, computational systems biology and biological pathway simulation with Petri nets, and physiologically based pharmacokinetic modeling and pharmacodynamic models. The final chapter explores Monte Carlo computing techniques for statistical inference.

This book offers a systematic treatment of computer simulation in drug development. It not only deals with the principles and methods of Monte Carlo simulation, but also the applications in drug development, such as statistical trial monitoring, prescription drug marketing, and molecular docking.

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Product Details

ISBN-13:
9781439835920
Publisher:
Taylor & Francis
Publication date:
09/24/2010
Series:
Chapman & Hall/CRC Biostatistics Series
Pages:
564
Product dimensions:
6.20(w) x 9.40(h) x 1.40(d)

Meet the Author

Mark Chang is the executive director of biostatistics and data management at AMAG Pharmaceuticals in Lexington, Massachusetts. Dr. Chang is an elected fellow of the American Statistical Association. He is the author of the best-selling Adaptive Design Theory and Implementation Using SAS and R and co-author of the best-selling Adaptive Design Methods in Clinical Trials.

Table of Contents

Preface

1 Simulation, Simulation Everywhere 1

1.1 Modeling and Simulation 1

1.1.1 The Art of Simulations 1

1.1.2 Genetic Programming in Art Simulation 2

1.1.3 Artificial Neural Network in Music Machinery 3

1.1.4 Bilingual Bootstrapping in Word Translation 5

1.2 Introductory Monte Carlo Examples 6

1.2.1 USA Territory 6

1.2.2 Π Simulation 7

1.2.3 Definite Integrals 9

1.2.4 Fastest Route 11

1.2.5 Economic Globalization 13

1.2.6 Percolation and Chaos 14

1.2.7 Fish Pond 16

1.2.8 Competing Risks 18

1.2.9 Pandemic Disease Modeling 19

1.2.10 Random Walk and Integral Equation 20

1.2.11 Financial Index and αStable Distribution 23

1.2.12 Nonlinear Equation System Solver 25

1.2.13 Stochastic Optimization 26

1.2.14 Symbolic Regression 28

1.3 Simulations in Drug Development 31

1.3.1 Challenges in the Pharmaceutical Industry 31

1.3.2 Classification of Simulations in Drug Development 32

1.4 Summary 33

1.5 Exercises 36

2 Virtual Sampling Techniques 39

2.1 Uniform Random Number Generation 39

2.2 General Sampling Methods 40

2.2.1 Inverse CDF Method 40

2.2.2 Acceptance-Rejection Method 41

2.2.3 Sampling of Order Statistics 43

2.2.4 Markov Chain Monte Carlo 44

2.2.5 Gibbs Sampling 46

2.2.6 Sampling from a Distribution in a Simplex 47

2.2.7 Sampling from a Distribution on a Hyperellipsoid 48

2.3 Efficiency Improvement in Virtual Sampling 48

2.3.1 Moments and Variable Transformation 48

2.3.2 Importance Sampling 49

2.3.3 Control Variables 50

2.3.4 Stratification 51

2.4 Sampling Algorithms for Specific Distributions 53

2.4.1 Uniform Distribution 53

2.4.2 Triangular Distribution 54

2.4.3 Normal Distribution 55

2.4.4 Gamma Distribution 56

2.4.5 Beta Distribution 58

2.4.6 Snedecor's F-Distribution 61

2.4.7 Chi-Square Distribution 62

2.4.8 Student Distribution 62

2.4.9 Exponential Distribution 63

2.4.10 Weibull Distribution 64

2.4.11 Inverse Gaussian Distribution 65

2.4.12 Laplace Distribution 66

2.4.13 Multivariate Normal Distribution 67

2.4.14 Equal Distribution 67

2.4.15 Binomial Distribution 68

2.4.16 Poisson Distribution 69

2.4.17 Negative Binomial 70

2.4.18 Geometric Distribution 71

2.4.19 Hypergeometric Distribu

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