Bayesian Analysis with R for Drug Development Concepts, Algorithms, and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development. Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical, clinical, and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.

SECTION IBackground1. Bayesian Statistics in Pharmaceutical DevelopmentIntroductionOverview of Drug DevelopmentBasic ResearchDrug DiscoveryFormulationLaboratory Test MethodsPre-Clinical StudiesClinical DevelopmentTranslational ResearchChemical Manufacturing and ControlRegulatory RegistrationStatistics in Drug Research and DevelopmentBayesian StatisticsOpportunities of Bayesian ApproachPre-Clinical DevelopmentCMC DevelopmentClinical TrialsChallenges of Bayesian ApproachObjection to BayesianRegulatory HurdlesConcluding Remarks 2. Basics of Bayesian StatisticsIntroductionStatistical InferenceResearch QuestionsProbability DistributionFrequentist MethodsBayesian InferenceSelection of PriorsBayesian ComputationMonte Carlo SimulationExampleMarkov Chain Monte CarloComputation ToolsBUGS and JAGSSAS PROC MCMCUtility of JAGSConcluding Remarks 3. Bayesian Estimation of Sample Size and PowerIntroductionSample Size DeterminationFrequentist MethodsBayesian ConsiderationsBayesian ApproachesPower and Sample SizeInterim AnalysisFutility and Sample SizeCase ExampleModelling of Overall SurvivalMaximum Likelihood EstimationFutility AnalysisConcluding Remarks SECION II Pre-Clinical and Clinical Research4. Pre-Clinical Efficacy StudyIntroductionEvaluation of Lab-Based Drugs in CombinationBackgroundStatistical MethodsAntiviral CombinationEvaluation of Fixed Dose CombinationBayesian Survival AnalysisLimitations of Animal DataCurrent MethodsBayesian SolutionCase ExampleConcluding Remarks 5. Bayesian Adaptive Design for Phase I Dose-Finding StudiesIntroductionAlgorithm-Based Design3+3 DesignAlternate Algorithm-Based DesignsAdvantages and Disadvantages of Algorithm-Based DesignsModel Based DesignsContinual Reassessment MethodsCRM for Phase I Cancer TrialsEscalation with Overdose ControlEscalation Based on Toxicity IntervalsConcluding Remarks 6. Design and Analysis of Phase II Dose-Ranging StudiesIntroductionPhase II Dose-Ranging StudiesCriticisms of Traditional MethodsModel-Based ApproachesEstimating Predictive Precision and Assurance for New TrialCOPD StudyEstimation MethodConcluding Remarks 7. Bayesian Multi-Stage Designs for Phase II Clinical TrialsIntroductionPhase II Clinical TrialsMulti-Stage DesignsFrequentist ApproachesBayesian MethodsBayesian Single-Arm TrialsContinuous Monitoring of Single-Arm TrialsComparative Phase II StudiesExamplesOncology TrialMulti-Stage Bayesian DesignConcluding Remarks SECTION III Chemistry, Manufacturing, and Control8. Analytical MethodsIntroductionMethod ValidationBackgroundStudy Design for Validation of Accuracy and PrecisionCurrent Statistical MethodsTotal Error ApproachBayesian SolutionsExampleMethod TransferBackgroundModelLinear ResponseCase ExampleConcluding Remarks 9. Process DevelopmentIntroductionQuality by DesignCritical Quality AttributesRisk of OncogenicityBayesian Risk AssessmentModeling Enzyme Cutting EfficiencyBayesian SolutionExampleDesign SpaceDefinitionStatistical Methods for Design SpaceBayesian Design SpaceExampleProcess ValidationRisk-Based Lifecycle ApproachMethod Based on Process CapabilityMethod Based on Predictive PerformanceDetermination of Number of PPQ BatchesConcluding Remarks 10. StabilityIntroductionStability StudyShelf-Life EstimationCurrent MethodsBayesian ApproachesExamplesSelection of Stability DesignBayesian CriterionSetting Release LimitsConcluding Remarks 11. Process ControlIntroductionQuality Control and ImprovementControl ChartsTypes of Control ChartsShewhart I-MR ChartEWMA Control ChartCUSUM Control ChartJ-ChartMultivariate Control ChartBayesian Control ChartsControl Chart for Data with CensoringControl Chart for Discrete DataControl Limit for Aberrant DataProduct Quality Control Based on Safety Data from SurveillanceConcluding Remarks

Harry Yang is Senior Director and Head of Statistical Sciences at MedImmune. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published six statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. Dr. Yang is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick is Director of Statistical Sciences at MedImmune. He has extensively contributed statistical methods to the biopharmaceutical literature. Dr. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. He served on IPAC-RS and has chaired several national statistical conferences.