Dose Finding by the Continual Reassessment Method (Chapman & Hall/CRC Biostatistics Series)
By: Ying Kuen Cheung (author)Hardback
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As clinicians begin to realize the important role of dose-finding in the drug development process, there is an increasing openness to "novel" methods proposed in the past two decades. In particular, the Continual Reassessment Method (CRM) and its variations have drawn much attention in the medical community, though it has yet to become a commonplace tool. To overcome the status quo in phase I clinical trials, statisticians must be able to design trials using the CRM in a timely and reproducible manner.
A self-contained theoretical framework of the CRM for researchers and graduate students who set out to learn and do research in the CRM and dose-finding methods in general, Dose Finding by the Continual Reassessment Method features:
Real clinical trial examples that illustrate the methods and techniques throughout the bookDetailed calibration techniques that enable biostatisticians to design a CRM in timely mannerLimitations of the CRM are outlined to aid in correct use of method
This book supplies practical, efficient dose-finding methods based on cutting edge statistical research. More than just a cookbook, it provides full, unified coverage of the CRM in addition to step-by-step guidelines to automation and parameterization of the methods used on a regular basis. A detailed exposition of the calibration of the CRM for applied statisticians working with dose-finding in phase I trials, the book focuses on the R package `dfcrm' for the CRM and its major variants.
The author recognizes clinicians' skepticism of model-based designs, and addresses their concerns that the time, professional, and computational resources necessary for accurate model-based designs can be major bottlenecks to the widespread use of appropriate dose-finding methods in phase I practice. The theoretically- and empirically-based methods in Dose Finding by the Continual Reassessment Method will lessen the statistician's burden and encourage the continuing development and implementation of model-based dose-finding methods.
Department of Biostatistics, Columbia University, USA
FundamentalsIntroductionDose Finding in Clinical TrialsThe Maximum Tolerated DoseAn Overview of MethodologyBibliographic NotesExercises and Further ResultsThe Continual Reassessment MethodIntroductionOne-Stage Bayesian CRMTwo-Stage CRMSimulating CRM TrialsPractical ModificationsBibliographic NotesExercises and Further ResultsOne-Parameter Dose-ToxicityModelsIntroduction? ?-Equivalent ModelsModel AssumptionsProof of Theorem 4.1Exercises and Further ResultsTheoretical PropertiesIntroductionCoherenceLarge-Sample PropertiesProofsExercises and Further ResultsEmpirical PropertiesIntroductionOperating CharacteristicsA Nonparametric Optimal BenchmarkExercises and Further ResultsDesign CalibrationSpecifications of a CRM DesignIntroductionSpecifying the Clinical ParametersA Roadmap for Choosing the Statistical ComponentThe Trial-and-Error Approach: Two Case StudiesInitial Guesses of Toxicity ProbabilitiesIntroductionHalf-width (? ?? ?) of Indifferent IntervalCalibration of ? ?? ?77Case Study: The Bortezomib TrialExercises and Further ResultsLeast Informative Normal PriorIntroductionLeast Informative PriorCalibration of ? ?? ?? ?93Optimal Least Informative ModelRevisiting the Bortezomib TrialInitial DesignIntroductionOrdering of Dose SequencesBuilding Reference Initial DesignsPractical IssuesCase Study: NeuSTARTExercises and Further ResultsCRM and BeyondThe Time-to-Event CRMIntroductionThe Basic ApproachNumerical IllustrationEnrollment SchedulingTheoretical PropertiesTwo-Stage DesignBibliographicNotesExercises and Further ResultsCRM withMultiparameter ModelsIntroductionCurve-Free MethodsRigidityTwo-Parameter CRMBibliographicNotesExercise and Further ResultsWhen the CRM FailsIntroductionTrade-Off Perspective of MTDBivariate Dose FindingStochastic ApproximationIntroductionThe Past LiteratureThe Present RelevanceThe Future ChallengeAssumptions on M(x) and Y (x)Exercises and Further ResultsReferencesIndex
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- ID: 9781420091519
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