MARC Record from marc_columbia

LEADER: 13587cam a2200793 a 4500
001 15080395
005 20220730230846.0
006 m o d
007 cr cnu---unuuu
008 101006s2011 flu ob 001 0 eng d
035 $a(OCoLC)ocn668400779
035 $a(NNC)15080395
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066 $c(S
016 7 $a018392737$2Uk
016 7 $a101531192$2DNLM
019 $a748682462$a860514569$a1065981587
020 $a9781439835937$q(electronic bk.)
020 $a1439835934$q(electronic bk.)
020 $z9781439835920$q(hardcover ;$qalk. paper)
020 $z1439835926$q(hardcover ;$qalk. paper)
035 $a(OCoLC)668400779$z(OCoLC)748682462$z(OCoLC)860514569$z(OCoLC)1065981587
037 $aTANDF_217676$bIngram Content Group
050 4 $aRM301.25$b.C46 2011eb
060 4 $aQV 736
072 7 $aMED$x023000$2bisacsh
072 7 $aMED$x071000$2bisacsh
072 7 $aMED$x072000$2bisacsh
072 7 $aMED$x058170$2bisacsh
082 04 $a615/.190113$222
049 $aZCUA
100 1 $aChang, Mark.
245 10 $aMonte Carlo simulation for the pharmaceutical industry :$bconcepts, algorithms, and case studies /$cMark Chang.
260 $aBoca Raton :$bCRC Press,$c©2011.
300 $a1 online resource (539 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aChapman & Hall/CRC biostatistics series ;$v36
504 $aIncludes bibliographical references (pages 487-501) and index.
520 $a"Preface Drug development, aiming at improving people's health, becomes more costly every year. The pharmaceutical industry must join its efforts with government and health professions to seek new, innovative, and cost- effective approaches in the development process. During this evolutionary process in the next decades, computer simulations will no doubt play a critical role. Computer simulation or Monte Carlo 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 virtually in every concern of engineering, science, mathematics, etc. In this book, I am going to present the concept, theory, algorithm, 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, Petrinet, genetic programming, resampling methods, and other strategies. These theories and methods either are necessary to carry out the simulations or make the simulations more efficient, even though there are many practical problems that can be simulated directly in ad hoc fashion without any theory of 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 the simplified versions of the real world problems. These simplifications are necessary because a single case could otherwise occupy the whole book, preventing readers from exploring broad issues"--Provided by publisher
505 0 $6880-01$aFront cover; Dedication; Contents; Preface; Chapter 1. Simulation, Simulation Everywhere; Chapter 2. Virtual Sampling Techniques; Chapter 3. Overview of Drug Development; Chapter 4. Meta-Simulation for the Pharmaceutical Industry; Chapter 5. Macro-Simulation for Pharmaceutical Research and Development; Chapter 6. Clinical Trial Simulation (CTS); Chapter 7. Clinical Trial Management and Execution; Chapter 8. Prescription Drug Commercialization; Chapter 9. Molecular Design and Simulation; Chapter 10. Disease Modeling and Biological Pathway Simulation; Chapter 11. Pharmacokinetic Simulation.
588 0 $aPrint version record.
650 0 $aDrug development$xComputer simulation.
650 0 $aMonte Carlo method.
650 0 $aDrugs$xDesign.
650 12 $aDrug Industry$xmethods
650 22 $aComputer Simulation
650 22 $aDrug Design
650 22 $aMonte Carlo Method
650 22 $aTechnology, Pharmaceutical$xmethods
650 6 $aMédicaments$xDéveloppement$xSimulation par ordinateur.
650 6 $aMéthode de Monte-Carlo.
650 6 $aSimulation par ordinateur.
650 6 $aMédicaments$xConception.
650 7 $asimulation.$2aat
650 7 $aMEDICAL$xDrug Guides.$2bisacsh
650 7 $aMEDICAL$xPharmacology.$2bisacsh
650 7 $aMEDICAL$xPharmacy.$2bisacsh
650 7 $aMEDICAL$xNursing$xPharmacology.$2bisacsh
650 7 $aDrugs$xDesign.$2fast$0(OCoLC)fst00898790
650 7 $aMonte Carlo method.$2fast$0(OCoLC)fst01025819
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aChang, Mark.$tMonte Carlo simulation for the pharmaceutical industry.$dBoca Raton : CRC Press, 2011$z9781439835920$w(DLC) 2010019530$w(OCoLC)624405718
830 0 $aChapman & Hall/CRC biostatistics series ;$v36.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15080395$zTaylor & Francis eBooks
880 00 $6505-01/(S$gMachine generated contents note:$g1.$tSimulation, Simulation Everywhere --$g1.1.$tModeling and Simulation --$g1.1.1.$tArt of Simulations --$g1.1.2.$tGenetic Programming in Art Simulation --$g1.1.3.$tArtificial Neural Network in Music Machinery --$g1.1.4.$tBilingual Bootstrapping in Word Translation --$g1.2.$tIntroductory Monte Carlo Examples --$g1.2.1.$tUSA Territory --$g1.2.2.$tΠ Simulation --$g1.2.3.$tDefinite Integrals --$g1.2.4.$tFastest Route --$g1.2.5.$tEconomic Globalization --$g1.2.6.$tPercolation and Chaos --$g1.2.7.$tFish Pond --$g1.2.8.$tCompeting Risks --$g1.2.9.$tPandemic Disease Modeling --$g1.2.10.$tRandom Walk and Integral Equation --$g1.2.11.$tFinancial Index and αStable Distribution --$g1.2.12.$tNonlinear Equation System Solver --$g1.2.13.$tStochastic Optimization --$g1.2.14.$tSymbolic Regression --$g1.3.$tSimulations in Drug Development --$g1.3.1.$tChallenges in the Pharmaceutical Industry --$g1.3.2.$tClassification of Simulations in Drug Development --$g1.4.$tSummary --$g1.5.$tExercises --$g2.$tVirtual Sampling Techniques --$g2.1.$tUniform Random Number Generation --$g2.2.$tGeneral Sampling Methods --$g2.2.1.$tInverse CDF Method --$g2.2.2.$tAcceptance-Rejection Method --$g2.2.3.$tSampling of Order Statistics --$g2.2.4.$tMarkov Chain Monte Carlo --$g2.2.5.$tGibbs Sampling --$g2.2.6.$tSampling from a Distribution in a Simplex --$g2.2.7.$tSampling from a Distribution on a Hyperellipsoid --$g2.3.$tEfficiency Improvement in Virtual Sampling --$g2.3.1.$tMoments and Variable Transformation --$g2.3.2.$tImportance Sampling --$g2.3.3.$tControl Variables --$g2.3.4.$tStratification --$g2.4.$tSampling Algorithms for Specific Distributions --$g2.4.1.$tUniform Distribution --$g2.4.2.$tTriangular Distribution --$g2.4.3.$tNormal Distribution --$g2.4.4.$tGamma Distribution --$g2.4.5.$tBeta Distribution --$g2.4.6.$tSnedecor's F-Distribution --$g2.4.7.$tChi-Square Distribution --$g2.4.8.$tStudent Distribution --$g2.4.9.$tExponential Distribution --$g2.4.10.$tWeibull Distribution --$g2.4.11.$tInverse Gaussian Distribution --$g2.4.12.$tLaplace Distribution --$g2.4.13.$tMultivariate Normal Distribution --$g2.4.14.$tEqual Distribution --$g2.4.15.$tBinomial Distribution --$g2.4.16.$tPoisson Distribution --$g2.4.17.$tNegative Binomial --$g2.4.18.$tGeometric Distribution --$g2.4.19.$tHypergeometric Distribution --$g2.4.20.$tMultinomial Distribution --$g2.5.$tSummary --$g2.6.$tExercises --$g3.$tOverview of Drug Development --$g3.1.$tIntroduction --$g3.2.$tDrug Discovery --$g3.2.1.$tTarget Identification and Validation --$g3.2.2.$tIrrational Approach --$g3.2.3.$tRational Approach --$g3.2.4.$tBiologics --$g3.2.5.$tNanomedicine --$g3.3.$tPreclinical Development --$g3.3.1.$tObjectives of Preclinical Development --$g3.3.2.$tPharmacokinetics --$g3.3.3.$tPharmacodynamics --$g3.3.4.$tToxicology --$g3.4.$tClinical Development --$g3.4.1.$tOverview of Clinical Development --$g3.4.2.$tClassical Clinical Trial Paradigm --$g3.4.3.$tAdaptive Trial Design --$g3.4.4.$tClinical Trial Protocol --$g3.5.$tSummary --$g3.6.$tExercises --$g4.$tMeta-Simulation for the Pharmaceutical Industry --$g4.1.$tIntroduction --$g4.1.1.$tCharacteristics of Meta-Simulation --$g4.1.2.$tMacroeconomics --$g4.1.3.$tMicroeconomics --$g4.1.4.$tHealth Economics and Pharmacoeconomics --$g4.1.5.$tProfitability of the Pharmaceutical Industry --$g4.2.$tGame Theory Basics --$g4.2.1.$tPrisoners' Dilemma --$g4.2.2.$tExtensive Form --$g4.2.3.$tNash Equilibrium --$g4.2.4.$tMixed Strategy --$g4.2.5.$tGame with Multiple Options --$g4.2.6.$tOligopoly Model --$g4.2.7.$tGames with Multiple Equilibria --$g4.2.8.$tCooperative Games --$g4.2.9.$tPareto Optimum --$g4.2.10.$tMultiple-Player and Queuing Games --$g4.3.$tPharmaceutical Games --$g4.3.1.$tTwo-Player Pharmaceutical Game --$g4.3.2.$tMixed n-player Pharmaceutical Game --$g4.3.3.$tBayesian Adaptive Gaming Strategy --$g4.3.4.$tPharmaceutical Partnerships --$g4.4.$tPrescription Drug Global Pricing --$g4.4.1.$tPrescription Drug Price Policies --$g4.4.2.$tDrug Pricing Strategy --$g4.4.3.$tCost Projection of Drug Development --$g4.5.$tSummary --$g4.6.$tExercises --$g5.$tMacro-Simulation for Pharmaceutical Research and Development --$g5.1.$tSequential Decision Making --$g5.1.1.$tDescriptive and Normative Decisions --$g5.1.2.$tSequential Decision Problem --$g5.1.3.$tBackwards Induction --$g5.2.$tMarkov Decision Process --$g5.2.1.$tMarkov Chain --$g5.2.2.$tMarkov Decision Process --$g5.2.3.$tDynamic Programming --$g5.3.$tPharmaceutial Decision Process --$g5.3.1.$tMDP for a Clinical Development Program --$g5.3.2.$tMarkov Decision Tree and Out-Licensing --$g5.3.3.$tResearch and Development Portfolio Optimization --$g5.4.$tExtension of the Markov Decision Process --$g5.4.1.$tQ-Learning --$g5.4.2.$tBayesian Learning Process --$g5.4.3.$tBayesian Decision Theory --$g5.4.4.$tBayesian Stochastic Decision Process --$g5.4.5.$tOne-Step Forward Approach --$g5.4.6.$tPartially Observable Markov Decision Processes --$g5.5.$tSummary --$g5.6.$tExercises --$g6.$tClinical Trial Simulation (CTS) --$g6.1.$tClassical Trial Simulation --$g6.1.1.$tTypes of Trial Designs --$g6.1.2.$tClinical Trial Endpoint --$g6.1.3.$tSuperiority and Noninferiority Designs --$g6.1.4.$tTwo-Group Equivalence Trial --$g6.2.$tAdaptive Trial Simulation --$g6.2.1.$tAdaptive Trial Design --$g6.2.2.$tHypothesis-Based Adaptive Design Method --$g6.2.3.$tMethod Based on the Sum of p-values --$g6.2.4.$tMethod with Product of p-values --$g6.2.5.$tMethod with Inverse-Normal p-values --$g6.2.6.$tMethod Based on Brownian Motion --$g6.2.7.$tDesign Evaluation --- Operating Characteristics --$g6.2.8.$tSample Size Re-Estimation --$g6.2.9.$tPick-Winner Design --$g6.2.10.$tAdaptive Design Case Studies --$g6.3.$tSummary --$g6.4.$tExercises --$g7.$tClinical Trial Management and Execution --$g7.1.$tIntroduction --$g7.2.$tClinical Trial Management --$g7.2.1.$tCritical Path Analysis --$g7.2.2.$tLogic-Operations Research (OR) Networks---Shortest Path --$g7.2.3.$tLogic-AND Networks---Longest Path --$g7.2.4.$tAlgorithms for Critical Path Analysis --$g7.3.$tPatient Recruitment and Projection --$g7.3.1.$tClinical Trial Globalization --$g7.3.2.$tTarget Population and Site Selection --$g7.3.3.$tTime-to-Event Projection --$g7.4.$tRandomization --$g7.4.1.$tSimple Randomization --$g7.4.2.$tStratified Randomization --$g7.4.3.$tAdaptive Randomization --$g7.5.$tDynamic and Adaptive Drug Supply --$g7.5.1.$tConventional Drug Supply --$g7.5.2.$tDynamic and Adaptive Drug Supply --$g7.5.3.$tAdaptive Drug Supply --$g7.6.$tStatistical Trial Monitoring --$g7.6.1.$tNecessities of Trial Monitoring --$g7.6.2.$tData Monitor Committee Charter --$g7.6.3.$tStatistical Monitoring Tool --$g7.7.$tSummary --$g7.8.$tExercises --$g8.$tPrescription Drug Commercialization --$g8.1.$tDynamics of Prescription Drug Marketing --$g8.1.1.$tChallenges in Innovative Drug Marketing --$g8.1.2.$tStructure of the Pharmaceutical Market --$g8.1.3.$tCommon Marketing Strategies --$g8.2.$tStock-Flow Dynamic Model for Brand Planning --$g8.2.1.$tTraditional Approach --$g8.2.2.$tConcept of the Stock-Flow Model --$g8.2.3.$tPatient Flow --$g8.2.4.$tDoctor Adoption---Prescription --$g8.2.5.$tTreatment Attractions --$g8.2.6.$tDiffusion Model for Drug Adoption --$g8.2.7.$tStrategy Framework for NCE Introductions --$g8.2.8.$tData Source for Simulation --$g8.3.$tCompetitive Drug Marketing Strategy --$g8.3.1.$tPricing and Payer Strategies --$g8.3.2.$tMarketing Strategies after Patent Expiration --$g8.3.3.$tStochastic Market Game --$g8.4.$tCompulsory Licensing and Parallel Importation --$g8.4.1.$tLegal Complications of Drug Marketing --$g8.4.2.$tGrossman-Lai's Game Model --$g8.4.3.$tSequential Game of Drug Marketing --$g8.5.$tSummary --$g8.6.$tExercises --$g9.$tMolecular Design and Simulation --$g9.1.$tWhy Molecular Design and Simulation --$g9.1.1.$tLandscape of Molecular Design --$g9.1.2.$tInnovative Drug Discovery Approach --$g9.1.3.$tDrug-Likeness Concept.
852 8 $blweb$hEBOOKS