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Optimizing Treatment of Rectal Cancer Using a Markov Decision Model

Optimizing Treatment of Rectal Cancer Using a Markov Decision Model
使用马尔可夫决策模型优化直肠癌的治疗
批准号:
7661770
负责人:
George J Chang
金额:
$17.06万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-07-01 至 2014-06-30
关键词:
AccountingActive LearningAdenocarcinomaAdjuvantAdjuvant ChemotherapyAdjuvant RadiotherapyAffectBiometryBoard CertificationCancer CenterCancer ControlCantorCharacteristicsChronicClinicalClinical ResearchClinical TrialsColonColorectal SurgeryCommittee MembersConsensusDataDecision AnalysisDecision MakingDecision ModelingDevelopment PlansDiagnosisDiagnostic Neoplasm StagingDiseaseDistant MetastasisDoctor of MedicineDoctor of PhilosophyEducationEffectivenessEvaluationEventEvidence Based MedicineExcisionFox Chase Cancer CenterGuidelinesHealthHealth SciencesHealth Services ResearchIndividualInvestigationLifeLife ExpectancyLocally Advanced Malignant NeoplasmLong Term SurvivorshipMagnetic Resonance ImagingMalignant NeoplasmsMaster of Public HealthMaster of ScienceMedical SocietiesMentorsMentorshipModelingMorbidity - disease rateOperative Surgical ProceduresOutcomePatient PreferencesPatient SelectionPatientsPediatricsPelvisPhysiciansPostoperative PeriodProbabilityQuality of lifeRadiationRadiation therapyRandomized Controlled Clinical TrialsRandomized Controlled TrialsRecommendationRectal CancerRectumRecurrenceReportingResearchRiskRisk FactorsRoleScientistSiteSocietiesStagingSubgroupSurgeonSurveysTechniquesTestingTexasTotal Mesorectal ExcisionToxic effectTreatment-Related CancerUltrasonographyUncertaintyUnited StatesUnited States National Institutes of HealthUniversitiesWomanX-Ray Computed Tomographyalternative treatmentbasecancer epidemiologycancer therapycareer developmentchemotherapyclinically relevantcompare effectivenessconventional therapycosteffective therapyexperiencehealth related quality of lifeimprovedinnovationmenmortalitypreventprofessorprogramsresearch clinical testingsimulationstandard caresymposiumtreatment strategyvirtual

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DESCRIPTION (provided by applicant): This project proposes a mentored research program to develop George J. Chang, M.D. into an independent clinician-scientist specializing in cancer-related health services research that brings together cancer epidemiology, outcomes evaluation, and clinical decision analysis in order to improve the treatment of colon and rectal cancer and to prevent and control cancer treatment related morbidities. Dr. Chang is a surgeon with dual board certifications in General Surgery and Colon and Rectal Surgery and a clinical expertise in the subject of this proposal. The career development plan consists of didactic education including a Master of Science degree in Clinical Research, a broad-based experiential learning program, and a mentored research experience, Mentored research will be conducted under the direction of the mentorship committee composed of Scott B. Cantor, Ph.D., Professor of Biostatistics at M. D. Anderson Cancer Center and past-president of the Society for Medical Decision Making; Jon E. Tyson, M.D., M.P.H., Professor of Pediatrics and Director of the Center for Clinical Research and Evidence-Based Medicine, University of Texas Health Sciences Center; and J. Robert Beck, M.D., Senior Vice President and Chief Academic Officer, Fox Chase Cancer Center will serve as an off-site mentor and committee member. The proposed research focuses on applied cancer control by developing toxicity minimized and quality-of-life preserving strategies for the treatment of patients with rectal cancer. Rectal cancer is a common disease for which the current treatment is highly effective but can be associated with high rates of treatment-related toxicity with significant patient quality-of-life and societal costs. Unfortunately, all recent attempts at traditional clinical trials based on investigation within the United States have; failed. The proposed research will use well-established advanced modeling techniques and patient preferences research to perform a virtual randomized controlled clinical trial to determine the least toxic and most clinically effective treatment strategies or the tens of thousands of patients affected by this common cancer each year. RELEVANCE: Current treatment strategies for rectal cancer do not account for advances in diagnosis and treatment or for the wide variety of patient related differences that affect outcomes. This proposal will help improve the selection of patients with rectal cancer for preoperative chemoradiotherapy in order that patients will experience less chronic toxicity, physicians will have evidence on which to base treatment recommendations, and society will encumber fewer treatment and toxicity related costs.
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Optimizing Treatment of Rectal Cancer Using a Markov Decision Model
Optimizing Treatment of Rectal Cancer Using a Markov Decision Model
Optimizing Treatment of Rectal Cancer Using a Markov Decision Model
Optimizing Treatment of Rectal Cancer Using a Markov Decision Model
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