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

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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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Editorial Reviews

From the Publisher
"Overall, the book does not only cover a very broad range of different topics but manages to explain these coherently. … this book is not only of interest for scientists in the pharmaceutical industry but also for academia due to its thorough presentation."
—Frank Emmert-Streib, Statistical Methods in Medical Research, 21(6), 2012

"… well written and easy to read. … this book is worthwhile reading as a long introduction to Monte Carlo simulation and its eventual application in pharmaceutical industry. It can convince people to consider this methodology …"
—Sophie Donnet, International Statistical Review, 2012

"This is an ambitious book covering a very wide array of topics … the theoretical presentation is reliable and sophisticated … the ability of the author to condense such a broad array of topics, and to present them in a cohesive manner, is quite impressive, and means that the book will contain information of relevance to a wide audience. … Many statisticians working in the pharmaceutical industry will benefit from having access to a copy of this book. Some statisticians working outside the industry may also benefit from having access to a copy, particularly those working in areas overlapping with the pharmaceutical industry, such as clinical science and health economics."
—Ian C. Marschner, Australian & New Zealand Journal of Statistics, 2011

"For industry statisticians, scientists, and software engineers and programmers, Chang, who works for a pharmaceutical company, details concepts, theories, algorithms, and case studies for carrying out computer simulations in the drug development process, from drug discovery to clinical trial aspects to commercialization. He covers analogy and simulation using examples from different areas, general sampling methods and the different stages of drug development, simulation approaches based on game theory and the Markov decision process, simulations in classical and adaptive trials, and challenges in clinical trial management and execution. He then addresses 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, ending with Monte Carlo computing techniques for statistical inference."
SciTech Book News, February 2011

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Product Details

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.

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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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