Real-World Evidence in Drug Development and Evaluation

Using Real-World Evidence to Transform Drug Development: Opportunities and ChallengesIntroductionTraditional Drug Development ParadigmDrug Development ProgressLimitations of Traditional Randomized Controlled TrialsReal-World Data and Real-World EvidenceReal-World DataReal-World EvidenceDifferences between RWE and Outcomes of RCTRegulatory PerspectiveProductivity ChallengeFDA Critical Path InitiativeRegulatory Perspectives Pertaining to RWE Historical Approval Based on RWEAccess to RWDOpportunities of RWE in Drug DevelopmentEarly DiscoveryClinical Study Design and FeasibilityStudy ExecutionMarketing ApplicationProduct LaunchProduct Life Cycle ManagementChallenges with RWEData Access and QualityTechnological BarriersMethodological ChallengesLack of Data TalentsRegulatory RisksConcluding RemarksReferencesEvidence Derived from Real-World Data: Utility, Constraints, and CautionsWhat Is RWD in the Context of Drug Development and Clinical Practice?Why Is RWD Important?For What Purposes Can RWD Be Useful?What Study Designs and Statistical Methods Will Be Necessary to Ensure High-Quality RWE?Some Application ExamplesSummaryReferencesReal-World Evidence from Population-Based Cancer Registry DataIntroductionPopulation-Based Cancer RegistryCancer Incidence and Mortality RatesPopulation-Based Cancer SurvivalStatistical Methods for Population-Based Cancer DataThe Spatial Pattern of Cancer Incidence and MortalityTrends of Cancer Incidence and Mortality RatesCancer Survival Analysis and PredictionsApplications to Small Cell Lung CancerSpatial Patterns and Trends of SCLC Incidence and MortalityTrends of SCLC Survival Using Population-Based Registry DataExtrapolation of Long-Term Survival for an SCLC Clinical TrialSummaryBibliographyExternal Control Using RWE and Historical Data in Clinical DevelopmentIntroduction of Using RWE and Historical Data in Clinical DevelopmentSingle-Arm Trial Using External Control for Initial IndicationStrensiq® for the Treatment of Patients with Perinatal, Infantile, and Juvenile Onset Hypophosphatasia (HPP)Brineura® for a Specific Form of Batten DiseaseBavencio® for Patients with Metastatic Merkel Cell Carcinoma (MCC)Comparison Across Trials with External Control for Label ExpansionVelcade® Label Expansion for Patients with Relapsed and/or Progressive Multiple MyelomaBlincyto® Label Expansion for Minimal Residual Disease Positive (MRD+) Acute Lymphoblastic Leukemia (ALL)Important Considerations When Designing Studies and Analyzing Data Using External Control in Clinical DevelopmentStudy SelectionComparability of DataMethods for PS ModelMatching by PSStratification by PSIPTWAnother Example Using Stratification by PSMethods for Baseline Covariate Selection and CheckingSensitivity AnalysisProspectively Plan and Objectively Study DesignAcknowledgmentsReferencesBayesian Methods for Evaluating Drug Safety with Real-World EvidenceIntroductionBayesian Sensitivity Analysis of an Unobserved ConfounderBayesian Sensitivity AnalysisEffect of Spermicide Use on Birth DefectsConcluding RemarksMeta-Analysis of Drug Safety DataMeta-Analysis for Evidence SynthesisSuicidal Risk of BrodalumabStatistical Evaluation of Suicidal RiskConcluding RemarksSummaryReferencesReal-World Evidence for Coverage and Payment DecisionsIntroductionDefining ValueContracting Trends/Value-Based AgreementsOutcomes-Based ContractsFinancial-Based AgreementsAlternative/Innovative Payment ModelsImportance of RWE for Demonstrating ValueRWE for Product EffectivenessRWE for Product SafetyRWE for HEOR OutcomesRWE for Burden of DiseaseUse of RWE by Payers and Health Technology Assessment AgenciesTargeted Literature ReviewFindings from the Review of Studies on Trends in Use of RWE by PayersRecent Case Studies of Use of RWE in HTAsEffectiveness and Adherence in a Real-World SettingNatural History of the Disease and Long-Term Effectiveness for Ultra-Orphan ProductsReferencesCausal Inference for Observational Studies/Real-World DataCausal Inference with Real-World DataPropensity Score Adjustment for Observational StudiesPropensity Score MatchingMatching DesignMatching AlgorithmPost-Matching Balance CheckingPost-Matching Balance CheckingPropensity Score WeightingPropensity Score StratificationSensitivity Analysis for Hidden BiasThe Setup of Sensitivity AnalysisSensitivity Analysis Based on Randomization InferenceModel-Assisted Sensitivity AnalysisCase Study: Propensity Score Matching Design and Sensitivity Analysis for Trauma Care EvaluationPropensity Score MatchingBalance CheckingInference Under IgnorabilitySimultaneous Sensitivity AnalysisAcknowledgmentReferencesIntroduction to AI and Overview of Breakthroughsof AI in Drug DevelopmentWhy AI?What Is AI and How Do You Build an AI System?.AI in Drug DevelopmentA Minimalist Overview of Deep Learning MethodsWhy Deep Learning?What Is Deep Learning?What Makes a Good Deep Learning Application?Introduction of the Big Data in Clinical Space: EHRChallenges of Analyzing EHRA Case Study Using Deep Learning to Analyze EHRAlignment of Diabetic PatientsConvolutional Layers for EHR DataText Information from Unstructured DataMissing Data ImputationContinuous Endpoints: HbAc, BMISurvival Endpoint: MACEVirtual Twin MethodIntroduction to Cloud ComputingWhy Cloud Computing?How to Choose Cloud?ReferencesIndex
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