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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:
8504976
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 TrialsColon CarcinomaColorectal SurgeryCommittee MembersConsensusDataDecision AnalysisDecision MakingDecision ModelingDevelopment PlansDiagnosisDiagnostic Neoplasm StagingDiseaseDistant MetastasisDoctor of MedicineDoctor of PhilosophyEffectivenessEvaluationEventEvidence Based MedicineExcisionFox Chase Cancer CenterGuidelinesHealthHealth SciencesHealth Services ResearchIndividualInstructionInvestigationLifeLife 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 HealthWomanX-Ray Computed Tomographyalternative treatmentbasecancer epidemiologycancer therapycareer developmentchemoradiationchemotherapyclinically relevantcompare effectivenessconventional therapycostdidactic educationeffective therapyexperiencehealth related quality of lifeimprovedinnovationmenmortalitypreventprofessorprogramsresearch clinical testingsimulationstandard caresymposiumtreatment strategyvirtual

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中文摘要
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英文摘要
This project proposes a mentored research program todejvelop 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 morb dities. Dr. Chang is a surgeon with dual board certifications in General Surgery and Colon and Rectal Sijrgery 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 af 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, Univers>ity 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 controI bydeveloping toxicity minimized and quality-of-life preserving strategies for the treatment of patients withrec;tal 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. Theproposed 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 clinicallyi effective treatment strategies orthetens ofthousands of patients affected by this common cancer each year. RELEVANCE (See instructions): 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.
期刊论文(30)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1002/cncr.25973
发表时间: 2011-08-15
期刊: CANCER
影响因子: 6.2
作者: [Tsai, Chiaojung Jillian, Crane, Christopher H., Skibber, John M., Rodriguez-Bigas, Miguel A., Chang, George J., Feig, Barry W., Eng, Cathy, Krishnan, Sunil, Maru, Dipen M., Das, Prajnan]
通讯作者: Das, Prajnan
DOI: 10.1097/coc.0000000000000150
发表时间: 2017-06
期刊: American journal of clinical oncology
影响因子: --
作者: [Park IJ, You YN, Skibber JM, Rodriguez-Bigas MA, Das P, Eng C, Kopetz S, Wolff RA, Crane CH, Krishnan S, Minsky B, Hu CY, Nguyen S, Chang GJ]
通讯作者: Chang GJ
Comparative effectiveness of laparoscopy vs open colectomy among nonmetastatic colon cancer patients: an analysis using the National Cancer Data Base.
腹腔镜检查与开腹结肠切除术在非转移性结肠癌患者中的疗效比较:使用国家癌症数据库进行的分析。
DOI: 10.1093/jnci/dju491
发表时间: 2015
期刊: Journal of the National Cancer Institute
影响因子: --
作者: [Zheng,Zhiyuan, Jemal,Ahmedin, Lin,ChunChieh, Hu,Chung-Yuan, Chang,GeorgeJ]
通讯作者: Chang,GeorgeJ
DOI: 10.1097/dcr.0b013e31827bd287
发表时间: 2013-05
期刊: Diseases of the colon and rectum
影响因子: 3.9
作者: [Bowles TL, Hu CY, You NY, Skibber JM, Rodriguez-Bigas MA, Chang GJ]
通讯作者: Chang GJ
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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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