A Simulation Model-based Framework to Support Oncology Guidelines and Practice

支持肿瘤学指南和实践的基于仿真模型的框架

• 批准号: 9977402
• 负责人: Jinani Jayasekera
• 金额: $ 17.54万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977402
• 关键词: Address; Affect; Age; American Society of Clinical Oncology; Award; Big Data; Cancer Diagnostics; Cancer Intervention and Surveillance Modeling Network; Cancer Model; Caring; Characteristics; Clinical; Clinical Practice Guideline; Complex; Coupled; Data; Data Discovery; Data Sources; Decision Making; Development; Diagnosis; Discipline; Disease; ERBB2 gene; Endocrine; Equilibrium; Event; Foundations; Funding; Future; Gene Expression Profile; Genomics; Goals; Grant; Guidelines; Health Policy; Individual; Institute of Medicine (U.S.); Institutes; Intervention; Knowledge; Left; Malignant Neoplasms; Mentors; Methods; Mission; Modeling; Molecular; Oncology; Outcome; Patients; Policies; Population; Process; Publications; Race; Recurrence; Research; Research Personnel; Research Training; Resources; Scientist; Solid; Source; Suggestion; Test Result; Testing; Toxic effect; Training; Translational Research; Translations; Treatment outcome; Uncertainty; Validation; Woman; Women' s Group; Work; aged; base; cancer care; cancer therapy; career; chemotherapy; clinical care; clinical decision-making; clinical development; clinical practice; comorbidity; data sharing; design; diverse data; experience; experimental study; follow-up; hormone receptor-positive; hormone therapy; individual patient; innovation; interest; knowledge base; malignant breast neoplasm; mathematical model; models and simulation; mortality; multidisciplinary; personalized cancer care; response; secondary analysis; shared decision making; skills; tool; treatment choice; treatment effect; trend; trial design; tumor; tumor progression; virtual laboratory; web interface

Abstract Personalized cancer care is complex in this unprecedented era of discovery and big data. The large and evolving knowledge base requires clinicians and policymakers to synthesize diverse data to design new trials, inform clinical guidelines, practice, and policy. The Institute of Medicine and others have recommended the use of simulation modeling in these situations to synthesize evidence and support clinical care. Simulation modeling involves the use of mathematical models to combine various sources of evidence to assess how interventions could alter the progression of a disease and affect outcomes. My overarching goal is to use this K99/R00 award to gain the necessary skills and experience to become an independent researcher and leader in the use of simulation modeling in oncology. This revised application includes training in modeling methods needed to fill gaps in my knowledge and use that training to build a new simulation model to address gene expression profile (GEP)-guided care for the nearly 180,000 US women annually diagnosed with early-stage breast cancer. The training aims are: Aim K1. Apply training in data discovery and synthesis to develop input parameters to model the effects of chemo- endocrine (vs. endocrine) therapy on cancer outcomes such as recurrence, mortality, and chemotherapy- related toxicity based on patient (e.g., age, race, comorbidity) and tumor (e.g., tumor size, grade) characteristics, and GEP test results; Aim K2. Apply training in simulation modeling to build a model combining input parameters from aim K1 to project cancer outcomes; and Aim K3. Apply training in uncertainty quantification to estimate the variability associated with modeled outcomes to place results in context for clinicians and guideline developers. The research aims are: Aim R1. Perform model validation to demonstrate the model's ability to reproduce predictions using an independent data source that was not used in aim K1; make necessary revisions to the model; Aim R2. Use the validated model to provide a summary of the balance of benefits and harms of chemotherapy in exemplar groups of women to support the development of clinical guidelines; and Aim R3. Create an interactive web-interface to provide model results on the effects of chemo-endocrine (vs. endocrine) therapy on cancer outcomes based on individual characteristics. I am uniquely qualified for this award given my strong track record of pilot funding, 21 high impact publications, a solid quantitative research foundation, commitment to a research career, and preliminary modeling research with my multidisciplinary mentoring team. The exceptional institutional resources coupled with the Cancer Intervention and Surveillance Modeling Network (CISNET) provide the ideal setting for this application. The integrated research and training will leave me poised to become an independent researcher using simulation modeling to assist in the translation of rapidly evolving knowledge into oncology care.

抽象的 在这个空前的发现和大数据时代，个性化的癌症护理很复杂。大而不断发展的 知识库要求临床医生和政策制定者合成各种数据以设计新试验，告知 临床准则，实践和政策。医学研究所和其他研究所建议使用 在这些情况下进行仿真建模，以综合证据并支持临床护理。仿真建模 涉及使用数学模型结合各种证据来评估干预措施 可以改变疾病的进展并影响结果。我的总体目标是使用此K99/R00奖 获得必要的技能和经验，以成为使用的独立研究人员和领导者 肿瘤学中的模拟建模。 此修订的应用程序包括培训在我的知识中填补空白所需的建模方法和使用的培训 该培训以建立一个新的模拟模型来解决基因表达概况（GEP）指导的护理 每年将近18万名美国妇女被诊断出患有早期乳腺癌。培训目的是：AIM K1。 在数据发现和合成中应用培训以开发输入参数，以模拟化学的影响 内分泌（与内分泌）疗法有关癌症的结局，例如复发，死亡率和化学疗法 - 基于患者（例如，年龄，种族，合并症）和肿瘤（例如肿瘤大小，等级）的相关毒性 特征和GEP测试结果； AIM K2。在模拟建模中应用培训以建立模型 结合从AIM K1到癌症结果的投入参数；并瞄准K3。应用培训 不确定性量化以估计与建模结果相关的可变性，以将结果放入 临床医生和指南开发人员的背景。研究目的是：AIM R1。执行模型验证 证明该模型使用未使用的独立数据源重现预测的能力 在AIM K1中；对模型进行必要的修订； AIM R2。使用经过验证的模型提供 妇女示例群体中化学疗法的益处和危害的平衡以支持发展 临床准则；和目标R3。创建一个交互式Web界面，以提供模型结果 基于个体特征的癌症结局的化学内分泌（与内分泌）疗法。 鉴于我的飞行员资金有很强的记录，21个高影响力，我拥有该奖项的独特资格 出版物，可靠的定量研究基金会，对研究职业的承诺以及初步 与我的多学科指导团队进行研究。卓越的机构资源耦合 伴随癌症干预和监视建模网络（CISNET）为此提供了理想的设置 应用。综合研究和培训将使我准备成为一名独立的研究人员 使用仿真建模来帮助将快速发展的知识转化为肿瘤学护理。